Search results for: linguistic intelligence

Authors: Andrew D. Henshaw, James M. Austin

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Introduction: Human trafficking is an increasingly serious transnational criminal enterprise and social security issue. Despite ongoing efforts to mitigate the phenomenon and a significant expansion of security scrutiny over past decades, it is not receding. This is true for many nations in Southeast Asia, widely recognized as the global hub for trafficked persons, including men, women, and children. Clearly, human trafficking is difficult to address because there are numerous drivers, causes, and motivators for it to persist, such as non-military and non-traditional security challenges, i.e., climate change, global warming displacement, and natural disasters. These make displaced persons and refugees particularly vulnerable. The issue is so large conservative estimates put a dollar value at around $150 billion-plus per year (Niethammer, 2020) spanning sexual slavery and exploitation, forced labor, construction, mining and in conflict roles, and forced marriages of girls and women. Coupled with corruption throughout military, police, and civil authorities around the world, and the active hands of powerful transnational criminal organizations, it is likely that such figures are grossly underestimated as human trafficking is misreported, under-detected, and deliberately obfuscated to protect those profiting from it. For example, the 2022 UN report on human trafficking shows a 56% reduction in convictions in that year alone (UNODC, 2022). Our Approach: To better understand this, our research utilizes a bespoke methodology. Applying a JAM (Juxtaposition Assessment Matrix), which we previously developed to detect flows of dark money around the globe (Henshaw, A & Austin, J, 2021), we now focus on the human trafficking paradigm. Indeed, utilizing a JAM methodology has identified key indicators of human trafficking not previously explored in depth. Being a set of structured analytical techniques that provide panoramic interpretations of the subject matter, this iteration of the JAM further incorporates behavioral and driver indicators, including the employment of Open-Source Artificial Intelligence (OS-AI) across multiple collection points. The extracted behavioral data was then applied to identify non-traditional indicators as they contribute to human trafficking. Furthermore, as the JAM OS-AI analyses data from the inverted position, i.e., the viewpoint of the traffickers, it examines the behavioral and physical traits required to succeed. This transposed examination of the requirements of success delivers potential leverage points for exploitation in the fight against human trafficking in a new and novel way. Findings: Our approach identified new innovative datasets that have previously been overlooked or, at best, undervalued. For example, the JAM OS-AI approach identified critical 'dark agent' lynchpins within human trafficking that are difficult to detect and harder to connect to actors and agents within a network. Our preliminary data suggests this is in part due to the fact that ‘dark agents’ in extant research have been difficult to detect and potentially much harder to directly connect to the actors and organizations in human trafficking networks. Our research demonstrates that using new investigative techniques such as OS-AI-aided JAM introduces a powerful toolset to increase understanding of human trafficking and transnational crime and illuminate networks that, to date, avoid global law enforcement scrutiny.

Keywords: human trafficking, open-source intelligence, transnational crime, human security, international human rights, intelligence analysis, JAM OS-AI, Dark Money

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Rebekah Granger Ellis, Pat J. Austin, Marc P. Bonis, Richard B. Speaker, Jr.

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Using two psychometric instruments, this study examined social and emotional intelligence and moral judgment levels of more than 300 gifted and talented high school students enrolled in arts-integrated, academic acceleration, and creative arts charter schools in an ethnically diverse large city in the southeastern United States. Gifted and talented individuals possess distinguishable characteristics; these frequently appear as strengths, but often serious problems accompany them. Although many gifted adolescents thrive in their environments, some struggle in their school and community due to emotional intensity, motivation and achievement issues, lack of peers and isolation, identification problems, sensitivity to expectations and feelings, perfectionism, and other difficulties. These gifted students endure and survive in school rather than flourish. Gifted adolescents face special intrapersonal, interpersonal, and environmental problems. Furthermore, they experience greater levels of stress, disaffection, and isolation than non-gifted individuals due to their advanced cognitive abilities. Therefore, it is important to examine the long-term effects of participation in various gifted and talented programs on the socio-affective development of these adolescents. Numerous studies have researched moral, social, and emotional development in the areas of cognitive-developmental, psychoanalytic, and behavioral learning; however, in almost all cases, these three facets have been studied separately leading to many divergent theories. Additionally, various frameworks and models purporting to encourage the different socio-affective branches of development have been debated in curriculum theory, yet research is inconclusive on the effectiveness of these programs. Most often studied is the socio-affective domain, which includes development and regulation of emotions; empathy development; interpersonal relations and social behaviors; personal and gender identity construction; and moral development, thinking, and judgment. Examining development in these domains can provide insight into why some gifted and talented adolescents are not always successful in adulthood despite advanced IQ scores. Particularly whether emotional, social and moral capabilities of gifted and talented individuals are as advanced as their intellectual abilities and how these are related to each other. This mixed methods longitudinal study examined students in urban gifted and talented charter schools for (1) socio-affective development levels and (2) whether a particular environment encourages developmental growth. Research questions guiding the study: (1) How do academically and artistically gifted 10th and 11th grade students perform on psychological scales of social and emotional intelligence and moral judgment? Do they differ from the normative sample? Do gender differences exist among gifted students? (2) Do adolescents who attend distinctive gifted charter schools differ in developmental profiles? Students’ performances on psychometric instruments were compared over time and by program type. Assessing moral judgment (DIT-2) and socio-emotional intelligence (BarOn EQ-I: YV), participants took pre-, mid-, and post-tests during one academic school year. Quantitative differences in growth on these psychological scales (individuals and school-wide) were examined. If a school showed change, qualitative artifacts (culture, curricula, instructional methodology, stakeholder interviews) provided insight for environmental correlation.

Keywords: gifted and talented programs, moral judgment, social and emotional intelligence, socio-affective education

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Authors: Modupe Beatrice Adeyinka

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European Languages are languages of literature, science and technology. Whereas, African languages are of literature, both written and oral, making it difficult for Yoruba, the African language of Kwa linguistic classification, to neatly and accurately translate European scientific and technological words, expressions and technologies. Unless a pragmatic and communicative approach is adopted, equivalence of European technical and scientific texts might be a mission impossible for Yoruba scholars. In view of the aforementioned difficult task, this paper tends to highlight the need for a thorough study and evaluation of English or French words, expressions, idiomatic expressions, technical and scientific terminologies then, trying to find ways of adopting them to Yoruba environment through interpretative translation.

Keywords: communication, high technology, translation, Yoruba language

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Authors: Ward Makhoul

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Code-switching (CS) is known as a ubiquitous phenomenon in multilingual societies and countries. Vernacular Palestinian Arabic (PA) variety spoken in Israel is among these languages, informally used for day-to-day conversations only. Such conversations appear to contain code-switched instances from Hebrew, the formal and dominant language of the country, even in settings where the need for CS seems to be unnecessary. This study examines the CS practices in PA and investigates the reason behind these CS instances in controlled settings and the correlation between bilingual dominance and CS. In the production-task interviews and Bilingual Language Profile test (BLP), there was a correlation between language dominance and CS; 13 participants were interviewed to elicit and analyze natural speech-containing CS instances, along with undergoing a BLP test. The acceptability judgment task observed the limits and boundaries of different code-switched linguistic structures.

Keywords: code-switching, Hebrew, Palestinian-Arabic, vernacular

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Authors: Abdelrhman Elagez, Rolla Monib

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This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, an online survey was conducted with 24 project managers and construction professionals, possessing experience ranging from 1 to 30 years (with an average of 6.38 years), to gather industry perspectives and concerns relating to AI integration. The survey results yielded several significant findings. Firstly, respondents exhibited a moderate level of familiarity (66.67%) with AI technologies, while the industry's readiness for AI deployment and current usage rates remained low at 2.72 out of 5. Secondly, the top-ranked barriers to AI adoption were identified as lack of awareness, insufficient knowledge and skills, data quality concerns, high implementation costs, absence of prior case studies, and the uncertainty of outcomes. Thirdly, the most significant risks associated with AI use in construction were perceived to be a lack of human control (decision-making), accountability, algorithm bias, data security/privacy, and lack of legislation and regulations. Additionally, the participants acknowledged the value of factors such as education, training, organizational support, and communication in facilitating AI integration within the industry. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: risk management, construction, artificial intelligence, technology

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Hoora Beheshti Haradasht, Abooali Golzary

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Environmental education plays a pivotal role in shaping the future stewards of our planet. Leveraging the power of artificial intelligence (AI) in this endeavor presents an innovative approach to captivate and educate elementary school children about environmental sustainability. This paper explores the application of AI technologies in designing interactive and personalized learning experiences that foster curiosity, critical thinking, and a deep connection to nature. By harnessing AI-driven tools, virtual simulations, and personalized content delivery, educators can create engaging platforms that empower children to comprehend complex environmental concepts while nurturing a lifelong commitment to protecting the Earth. With the pressing challenges of climate change and biodiversity loss, cultivating an environmentally conscious generation is imperative. Integrating AI in environmental education revolutionizes traditional teaching methods by tailoring content, adapting to individual learning styles, and immersing students in interactive scenarios. This paper delves into the potential of AI technologies to enhance engagement, comprehension, and pro-environmental behaviors among elementary school children. Modern AI technologies, including natural language processing, machine learning, and virtual reality, offer unique tools to craft immersive learning experiences. Adaptive platforms can analyze individual learning patterns and preferences, enabling real-time adjustments in content delivery. Virtual simulations, powered by AI, transport students into dynamic ecosystems, fostering experiential learning that goes beyond textbooks. AI-driven educational platforms provide tailored content, ensuring that environmental lessons resonate with each child's interests and cognitive level. By recognizing patterns in students' interactions, AI algorithms curate customized learning pathways, enhancing comprehension and knowledge retention. Utilizing AI, educators can develop virtual field trips and interactive nature explorations. Children can navigate virtual ecosystems, analyze real-time data, and make informed decisions, cultivating an understanding of the delicate balance between human actions and the environment. While AI offers promising educational opportunities, ethical concerns must be addressed. Safeguarding children's data privacy, ensuring content accuracy, and avoiding biases in AI algorithms are paramount to building a trustworthy learning environment. By merging AI with environmental education, educators can empower children not only with knowledge but also with the tools to become advocates for sustainable practices. As children engage in AI-enhanced learning, they develop a sense of agency and responsibility to address environmental challenges. The application of artificial intelligence in elementary environmental education presents a groundbreaking avenue to cultivate environmentally conscious citizens. By embracing AI-driven tools, educators can create transformative learning experiences that empower children to grasp intricate ecological concepts, forge an intimate connection with nature, and develop a strong commitment to safeguarding our planet for generations to come.

Keywords: artificial intelligence, environmental education, elementary children, personalized learning, sustainability

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Authors: Alaa Yehia, Ayatollah Yehia, Sherif Yehia

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There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market, and a lack of construction projects due to the impending or current recession. However, this recurring problem alludes to an intrinsic issue of the curriculum. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI) demands individuals who are proficient at utilizing it. Therefore, existing curriculums must adapt to this change in order to provide an education that is suitable for potential and current students. In this paper, In order to provide potential solutions for this issue, the analysis considers two possible implementations of high technology into the civil engineering curriculum. The first approach is to implement a course that introduces applications of high technology in Civil Engineering contexts. While the other approach is to intertwine applications of high technology throughout the degree. Both approaches, however, should meet requirements of accreditation agencies. In addition to the proposed improvement in civil engineering curriculum, a different pedagogical practice must be adapted as well. The passive learning approach might not be appropriate for Gen Z students; current students, now more than ever, need to be introduced to engineering topics and practice following different learning methods to ensure they will have the necessary skills for the job market. Different learning methods that incorporate high technology applications, like AI, must be integrated throughout the curriculum to make the civil engineering degree more attractive to prospective students. Moreover, the paper provides insight on the importance and approach of adapting the Civil Engineering curriculum to address the current low enrollment crisis that civil engineering departments globally, but specifically in the Middle East, are facing.

Keywords: artificial intelligence (AI), civil engineering curriculum, high technology, low enrollment, pedagogy

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Shahadat Hossain

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The United Nations (UN) declared Rohingya as the most oppressed nation in the world. The Rohingya's native place is Arakan, Myanmar, which is newly named Rakhine. The Rohingya have been forcibly migrated to Bangladesh, Malaysia, and other states for settlement for many years. Bangladesh has not been able to handle the pressure of Rohingya refugees, although it has been hosting Rohingya refugees for multiple decades. As a result, Rohingya refugees have been mixed with the local population. Some of the Rohingya people of Arakan already became citizens of Bangladesh after migrating to Bangladesh. The Rohingya have become Bangladeshis through intermarriage, kinship, labour, and business partnerships. Rohingya people preferred to settle in Bangladesh due to cultural, religious, and linguistic similarities. Some of the Rohingyas get an advantage also from the domestic political and voting equation of Bangladesh. This research tried to explore how the Rohingyas settled in Chattogram and Cox's Bazar and became one of the locals. The research sought to focus on their advantage, difficulties, and narrative.

Keywords: Rohingya, refugee, Bangladesh, Rohingya settlement

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Authors: Jawharah Alasmari

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In inflectional morphology of verb, tense and aspect indicate action’s time either past/present or future and their period whether completed or not. The usage and meaning of tense and aspect differ in Arabic and English, therefore is no simple one -to- one mapping from an Arabic verb inflected form an appropriate English translation depends on a range of features, including immediate and wider context of use. The Quranic Arabic Corpus includes seven alternative expertly crafted English translations of each Arabic verses, which provides a test dataset for the study of appropriate Arabic to English translations of verb tense and aspect. We applied Corpus Linguistics Methods in a theoretical study of exemplary verbs, to elicit candidate verbal contexts which influence the choice of English inflection for each verse.

Keywords: Corpus linguistics methods, Arabic verb, tense and aspect, English translations

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Authors: Javiera Espinosa, Patricia López, Noelia Santos, Nadia Loro, Esther Moraleda

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In recent years, there has been a notable increase in the number of investigations that establish that Down Syndrome and Autism Spectrum Disorder are diagnoses that can coexist together. However, there are also many studies that consider that both diagnoses present neuropsychological, linguistic and adaptive characteristics with a totally different profile. The objective of this research is to question whether there really can be a profile that encompasses both disorders or if they can be incompatible with each other. To this end, a review of the scientific literature of recent years has been carried out. The results indicate that the two lines collect opposite approaches. On the one hand, there is research that supports the increase in comorbidity between Down Syndrome and Autism Spectrum Disorder, and on the other hand, many investigations show a totally different general development profile between the two. The discussion focuses on discussing both lines of work and on proposing future lines of research in this regard.

Keywords: disability, language, speech, down syndrome

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Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Kaoutar Filahi

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This study was inspired by the necessity of giving forensic linguistics and phonetics more and more importance and the intention to explore those topics in an attempt to understand what the role of these disciplines really is in investigations of any nature. The goal is to analyze what are the achievements that those subjects have been able to reach, and what contribution they gave to the legal world; the analysis and study of those topics are supported by the recounting of real cases that have included forensic and phonetic linguistics. One of the most relevant cases is that of the Unabomber, an investigation that brought to light the importance and highlighted the importance this matter can have in difficult and time-consuming cases such as the one we have here. We also focus on the areas of expertise of those new branches of applied linguistics, focusing on what is the use of this new discipline in Italy and abroad and showing what could be the possible improvements that the Italian state could apply in order to be able to catch up with countries like Great Britain.

Keywords: forensic linguistic, forensic phonetics, investigation, criminalistics

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Authors: A. Noll, J. Roth, M. Scholz

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The importance of written language in a democratic society is non-controversial. Students with physical, learning, cognitive or developmental disabilities often have difficulties in understanding information which is presented in written language only. These students suffer from obstacles in diverse domains. In order to reduce such barriers in educational as well as in out-of-school areas, access to written information must be facilitated. Readability can be enhanced by linguistic simplifications like the application of easy-to-read language. Easy-to-read language shall help people with disabilities to participate socially and politically in society. The authors state, for example, that only short simple words should be used, whereas the occurrence of complex sentences should be avoided. So far, these guidelines were not empirically proved. Another way to reduce reading barriers is the use of visual support, for example, symbols. A symbol conveys, in contrast to a photo, a single idea or concept. Little empirical data about the use of symbols to foster the readability of texts exist. Nevertheless, a positive influence can be assumed, e.g., because of the multimedia principle. It indicates that people learn better from words and pictures than from words alone. A qualitative Interview and Eye-Tracking-Study, which was conducted by the authors, gives cause for the assumption that besides the illustration of single words, the visualization of complete sentences may be helpful. Thus, the effect of photos, which illustrate the content of complete sentences, is also investigated in this study. This leads us to the main research question which was focused on: Does the use of easy-to-read language and/or enriching text with symbols or photos facilitate pupils’ comprehension of learning tasks? The sample consisted of students with learning difficulties (N = 144) and students without SEN (N = 159). The students worked on the tasks, which dealt with introducing fractions, individually. While experimental group 1 received a linguistically simplified version of the tasks, experimental group 2 worked with a variation which was linguistically simplified and furthermore, the keywords of the tasks were visualized by symbols. Experimental group 3 worked on exercises which were simplified by easy-to-read-language and the content of the whole sentences was illustrated by photos. Experimental group 4 received a not simplified version. The participants’ reading ability and their IQ was elevated beforehand to build four comparable groups. There is a significant effect of the different setting on the students’ results F(3,140) = 2,932; p = 0,036*. A post-hoc-analyses with multiple comparisons shows that this significance results from the difference between experimental group 3 and 4. The students in the group easy-to-read language plus photos worked on the exercises significantly more successfully than the students who worked in the group with no simplifications. Further results which refer, among others, to the influence of the students reading ability will be presented at the ICERI 2018.

Keywords: inclusive education, mathematics education, easy-to-read language, photos, symbols, special educational needs

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Authors: Dina C. Castro, Rossana Boyd, Eugenia Papadaki

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Immigration within and across continents is currently a global reality. The number of people leaving their communities in search for a better life for them and their families has increased dramatically during the last twenty years. Therefore, young children of the 21st century around the World are growing up in diverse communities, exposed to many languages and cultures. One consequence of these migration movements is the increased linguistic diversity in school settings. Depending on the linguistic history and the status of languages in the communities (i.e., minority-majority; majority-majority) the instructional approaches will differ. This session will discuss how bilingualism is addressed in early education programs in both minority-majority and majority-majority language communities, analyzing experiences in three countries with very distinct societal and demographic characteristics: Peru (South America), the United States (North America), and Italy (European Union). The ultimate goal is to identify commonalities and differences across the three experiences that could lead to a discussion of bilingualism in early education from a global perspective. From Peru, we will discuss current national language and educational policies that have lead to the design and implementation of bilingual and intercultural education for children in indigenous communities. We will also discuss how those practices are being implemented in preschool programs, the progress made and challenges encountered. From the United States, we will discuss the early education of Spanish-English bilingual preschoolers, including the national policy environment, as well as variations in language of instruction approaches currently being used with these children. From Italy, we will describe early education practices in the Bilingual School of Monza, in northern Italy, a school that has 20 years promoting bilingualism and multilingualism in education. While the presentations from Peru and the United States will discuss bilingualism in a majority-minority language environment, this presentation will lead to a discussion on the opportunities and challenges of promoting bilingualism in a majority-majority language environment. It is evident that innovative models and policies are necessary to prevent inequality of opportunities for bilingual children beginning in their earliest years. The cross-cultural examination of bilingual education experiences for young children in three part of the World will allow us to learn from our success and challenges. The session will end with a discussion of the following question: To what extent are early care and education programs being effective in promoting positive development and learning among all children, including those from diverse language, ethnic and cultural backgrounds? We expect to identify, with participants to our session, a set of recommendations for policy and program development that could ensure access to high quality early education for all bilingual children.

Keywords: early education for bilingual children, global perspectives in early education, cross-cultural, language policies

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Authors: Mafawez Alharbi, Mahdi Jemmali

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E-learning is a developing area especially in education. E-learning can provide several benefits to learners. An intelligent system to collect all components satisfying user preferences is so important. This research presents an approach that it capable to personalize e-information and give the user their needs following their preferences. This proposal can make some knowledge after more evaluations made by the user. In addition, it can learn from the habit from the user. Finally, we show a walk-through to prove how intelligent process work.

Keywords: artificial intelligence, architecture, e-learning, software engineering, processing

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Authors: Mohsine Khazrouni

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The present study is an attempt to highlight the importance of adaptation in translation. To convey the message, the translator needs to take into account not only the text but also extra-linguistic factors such as the target audience. The present paper claims that adaptation is an unavoidable translation strategy when dealing with texts that are heavy with religious and cultural themes. The translation task becomes even more challenging when dealing with children’s literature as the audience are children whose comprehension, experience and world knowledge are limited. The study uses the Arabic translation of the short story ‘Christmas Every Day’ as a case study. The short story will be translated, and the pragmatic problems involved will be discussed. The focus will be on the issue of adaptation. i.e., the source text should be adapted to the target language audience`s social and cultural environment.

Keywords: pragmatic adaptation, Arabic translation, children's literature, equivalence

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Authors: L. Tizzi

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The aim of this contribution is to present the experience of the U.S. secondary school Social Justice High School (SoJo), part of the larger Campus of Little Village Lawndale High School (LVLHS) located in Chicago, Illinois (USA). This experience can be considered a concrete application of the principles of the educational perspective known, in the United States, as Social Justice Education, aimed at ensuring quality education and educational success for students from disadvantaged groups, particularly those characterized by “biculturalism”, i.e. students with a dual cultural and linguistic background. The contribution will retrace the historical and social events that led to the birth of the SoJo, explaining the principles and methods used by the school to achieve its objectives and giving also some statistical data.

Keywords: biculturalism, educational success, social justice education, social justice high school

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Authors: Ponghyung Lee

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This study seeks to investigate the variations occurring to Korean consonantal variations center around laryngeal features of the concerned sounds, to the exclusion of others. Our fundamental premise is that the weak contrast associated with concerned segments might be held accountable for the oscillation of the status quo of the concerned consonants. What is more, we assume that an array of notions as a measure of communicative efficiency of linguistic units would be significantly influential on triggering those variations. To this end, we have tried to compute the surprisal, entropic contribution, and relative contrastiveness associated with Korean obstruent consonants. What we found therein is that the Information-theoretic perspective is compelling enough to lend support our approach to a considerable extent. That is, the variant realizations, chronologically and stylistically, prove to be profoundly affected by a set of Information-theoretic factors enumerated above. When it comes to the biblical proper names, we use Georgetown University CQP Web-Bible corpora. From the 8 texts (4 from Old Testament and 4 from New Testament) among the total 64 texts, we extracted 199 samples. We address the issue of laryngeal feature variations associated with Korean obstruent consonants under the presumption that the variations stem from the weak contrast among the triad manifestations of laryngeal features. The variants emerge from diverse sources in chronological and stylistic senses: Christianity biblical texts, ordinary casual speech, the shift of loanword adaptation over time, and ideophones. For the purpose of discussing what they are really like from the perspective of Information Theory, it is necessary to closely look at the data. Among them, the massive changes occurring to loanword adaptation of proper nouns during the centennial history of Korean Christianity draw our special attention. We searched 199 types of initially capitalized words among 45,528-word tokens, which account for around 5% of total 901,701-word tokens (12,786-word types) from Georgetown University CQP Web-Bible corpora. We focus on the shift of the laryngeal features incorporated into word-initial consonants, which are available through the two distinct versions of Korean Bible: one came out in the 1960s for the Protestants, and the other was published in the 1990s for the Catholic Church. Of these proper names, we have closely traced the adaptation of plain obstruents, e. g. /b, d, g, s, ʤ/ in the sources. The results show that as much as 41% of the extracted proper names show variations; 37% in terms of aspiration, and 4% in terms of tensing. This study set out in an effort to shed light on the question: to what extent can we attribute the variations occurring to the laryngeal features associated with Korean obstruent consonants to the communicative aspects of linguistic activities? In this vein, the concerted effects of the triad, of surprisal, entropic contribution, and relative contrastiveness can be credited with the ups and downs in the feature specification, despite being contentiousness on the role of surprisal to some extent.

Keywords: entropic contribution, laryngeal feature variation, relative contrastiveness, surprisal

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Authors: Meshandren Naidoo, Amy Gooden

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Implantable brain-computer interface (BCI) technologies have developed to the point where brain-computer communication is possible. This has great potential in the medical field, as it allows persons who have lost capacities. However, ethicists and regulators call for a strict approach to these technologies due to the impact on personhood. This research demonstrates that the personhood debate is more nuanced and that where an African approach to personhood is used, it may produce results more favorable to the development and use of this technology.

Keywords: artificial intelligence, law, neuroscience, ethics

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Maria da Gloria Guara-Tavares

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This article reports a synthesis of the research in Brazilian Sign Language conducted from 2000 to 2017. The objective of the synthesis was to identify the most researched areas and the most used methodologies. Articles published in three Brazilian journals of Translation Studies, unpublished dissertations and theses were included in the analysis. Abstracts and the method sections of the papers were scrutinized. Sixty studies were analyzed, and overall results indicate that the research in Brazilian Sign Language has been fragmented in several areas such as linguistic aspects, facial expressions, subtitling, identity issues, bilingualism, and interpretation strategies. Concerning research methods, the synthesis reveals that most research is qualitative in nature. Moreover, results show that the cognitive aspects of Brazilian Sign Language seem to be poorly explored. Implications for a future research agenda are also discussed.

Keywords: Brazilian sign language, qualitative methods, research agenda, synthesis

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Authors: Laura Kahn, Paul Rodrigues, Onur Savas, Shannon Hahn

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An empirical understanding of an individual's personality expression can have a profound impact on organizations seeking to strengthen team performance and improve employee retention. A team's personality composition can impact overall performance. Creating a tiered information framework that leverages proxies for a user's social context and lexical and linguistic content provides insight into location-specific personality expression. We leverage the layered framework to examine domain-specific, psychological, and lexical cues within social media posts. We apply DistilBERT natural language transfer learning models with real world data to examine the relationship between Big Five personality expressions of people in Science, Technology, Engineering and Math (STEM) fields.

Keywords: big five, personality expression, social media analysis, workforce development

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Authors: Maya Lisa Aryanti, S. S. M. Hum

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Students usually make a lot of mistakes when they write their composition. The common mistake occurs when they write their own sentences. They perhaps can use certain verb and verb phrases properly, but on another occasion, they may choose wrong verb phrases. This paper illustrates ill-formed phrases, improper agreement between subject and verb and referent and reference in the students’ writings. The objectives of this research are to show possible variety of ill-formed phrases, to show frequent mistakes in S-V Agreement, and to show wrong reference in students’ writing. The methodology of this research is descriptive qualitative research. Some general linguistic theories and semantics are used in this paper. The results of this research concern to the number and the forms of possible ill-formed phrases, the types of Subject-Verb Agreement which are often applied incorrectly in a sentence and types of reference which are often used incorrectly.

Keywords: agreement, meaning, phrases, reference

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Anishur Rahman, Maria Areias, Diogo Simões, Ana Figeuiredo, Filipa Figueiredo, João Nunes

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The circular economy (CE) has gained increased awareness among academics, businesses, and decision-makers as it stimulates resource circularity in the production and consumption systems. A large epistemological study has explored the principles of CE, but scant attention eagerly focused on analysing how CE is evaluated, consented to, and enforced using economic metabolism data and business intelligent framework. Economic metabolism involves the ongoing exchange of materials and energy within and across socio-economic systems and requires the assessment of vast amounts of data to provide quantitative analysis related to effective resource management. Limited concern, the present work has focused on the regional flows pilot region from Portugal. By addressing this gap, this study aims to promote eco-innovation and sustainability in the regions of Intermunicipal Communities Região de Coimbra, Viseu Dão Lafões and Beiras e Serra da Estrela, using this data to find precise synergies in terms of material flows and give companies a competitive advantage in form of valuable waste destinations, access to new resources and new markets, cost reduction and risk sharing benefits. In our work, emphasis on applying artificial intelligence (AI) and, more specifically, on implementing state-of-the-art deep learning algorithms is placed, contributing to construction a business intelligent approach. With the emergence of new approaches generally highlighted under the sub-heading of AI and machine learning (ML), the methods for statistical analysis of complex and uncertain production systems are facing significant changes. Therefore, various definitions of AI and its differences from traditional statistics are presented, and furthermore, ML is introduced to identify its place in data science and the differences in topics such as big data analytics and in production problems that using AI and ML are identified. A lifecycle-based approach is then taken to analyse the use of different methods in each phase to identify the most useful technologies and unifying attributes of AI in manufacturing. Most of macroeconomic metabolisms models are mainly direct to contexts of large metropolis, neglecting rural territories, so within this project, a dynamic decision support model coupled with artificial intelligence tools and information platforms will be developed, focused on the reality of these transition zones between the rural and urban. Thus, a real decision support tool is under development, which will surpass the scientific developments carried out to date and will allow to overcome imitations related to the availability and reliability of data.

Keywords: circular economy, artificial intelligence, economic metabolisms, machine learning

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Authors: B. Karabuluter, O. Karaduman

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Traffic management and traffic planning are important issues, especially in big cities. Due to the increase of personal vehicles and the physical constraints of urban roads, the problem of transportation especially in crowded cities over time is revealed. This situation reduces the living standards, and it can put human life at risk because the vehicles such as ambulance, fire department are prevented from reaching their targets. Even if the city planners take these problems into account, emergency planning and traffic management are needed to avoid cases such as traffic congestion, intersections, traffic jams caused by traffic accidents or roadworks. In this study, in smart traffic management issues, proposed solutions using intelligent vehicles acting in cooperation with urban roads are examined. Traffic management is becoming more difficult due to factors such as fatigue, carelessness, sleeplessness, social behavior patterns, and lack of education. However, autonomous vehicles, which remove the problems caused by human weaknesses by providing driving control, are increasing the success of practicing the algorithms developed in city traffic management. Such intelligent vehicles have become an important solution in urban life by using 'swarm intelligence' algorithms and cooperative driving methods to provide traffic flow, prevent traffic accidents, and increase living standards. In this study, studies conducted in this area have been dealt with in terms of traffic jam, intersections, regulation of traffic flow, signaling, prevention of traffic accidents, cooperation and communication techniques of vehicles, fleet management, transportation of emergency vehicles. From these concepts, some taxonomies were made out of the way. This work helps to develop new solutions and algorithms for cities where intelligent vehicles that can perform cooperative driving can take place, and at the same time emphasize the trend in this area.

Keywords: intelligent traffic management, cooperative driving, smart driving, urban road, swarm intelligence, connected vehicles

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Authors: Asier Romero Andonegi, Irati De Pablo Delgado

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The objective of this study is to explore the different methodological procedures that are used to obtain information on the early linguistic development of children. To this end, two different methodological procedures were carried out on the same sample: on the one hand, the MacArthur-Bates Communicative Development Inventories, in its adaptations in Spanish and Basque; and on the other hand, longitudinal observation through professional software: ELAN and CHAT. The sample consists of 8 Basque children/ages 16 to 30 months with different mother tongue (L1). The results show the usefulness of inventories in obtaining information on the development of early communication and language skills, but also their limitations mostly focused on the interpretive overvaluation of their children’s lexical development.

Keywords: early language development, language evaluation, lexicon, MacArthur-Bates communicative development inventories

Procedia PDF Downloads 143