Search results for: cognitive intelligence

Authors: Felib Ayman Shawky Salem

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Experiential marketing means an unforgettable experience that remains deeply anchored in the customer's memory. Furthermore, customer satisfaction is defined as the emotional response to the experiences provided that relate to specific products or services purchased. Therefore, experiential marketing activities can influence the level of customer satisfaction and loyalty. In this context, the study aims to examine the relationship between experiential marketing, customer satisfaction and loyalty of beauty products in Konya. The results of this study showed that experiential marketing is an important indicator of customer satisfaction and loyalty and that experiential marketing has a significant positive impact on customer satisfaction and loyalty.

Keywords: sponsorship, marketing communication theories, marketing communication tools internet, marketing, tourism, tourism management corporate responsibility, employee organizational performance, internal marketing, internal customer experiential marketing, customer satisfaction, customer loyalty, social sciences.

Procedia PDF Downloads 60

Authors: S. Hameed, S. Anwer, M. Saad, M. Saady

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For autonomic recovery with server virtualization, a cogent plan that includes recovery techniques and backups with virtualized servers can be developed instead of assigning an idle server to backup operations. In addition to hardware cost reduction and data center trail, the disaster recovery plan can ensure system uptime and to meet objectives of high availability, recovery time, recovery point, server provisioning, and quality of services. This autonomic solution would also support disaster management, testing, and development of the recovery site. In this research, a workflow plan is proposed for supporting disaster recovery with virtualization providing virtual monitoring, requirements engineering, solution decision making, quality testing, and disaster management. This recovery model would make disaster recovery a lot easier, faster, and less error prone.

Keywords: autonomous intelligence, disaster recovery, cloud computing, server virtualization

Procedia PDF Downloads 156

Authors: Liu Si, Wang Chunmei, Liu Huangmei

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A prominent component of the semantic versus pragmatic debate, scalar implicature (SI) has been gaining great attention ever since it was proposed by Horn. The constant debate is between the structural and pragmatic approach. The former claims that generation of SI is costless, automatic, and dependent mostly on the structural properties of sentences, whereas the latter advocates both that such generation is largely dependent upon context, and that the process is costly. Many experiments, among which Katsos’s text comprehension experiments are influential, have been designed and conducted in order to verify their views, but the results are not conclusive. Besides, most of the experiments were conducted in English language materials. Katsos conducted one off-line and three on-line text comprehension experiments, in which the previous shortcomings were addressed on a certain extent and the conclusion was in favor of the pragmatic approach. We intend to test the results of Katsos’s experiment in Chinese scalar implicature. Four experiments in both off-line and on-line conditions to examine the generation and response time of SI in Chinese "yixie" (some) and "quanbu (dou)" (all) will be conducted in order to find out whether the structural or the pragmatic approach could be sustained. The study mainly aims to answer the following questions: (1) Can SI be generated in the upper- and lower-bound contexts as Katsos confirmed when Chinese language materials are used in the experiment? (2) Can SI be first generated, then cancelled as default view claimed or can it not be generated in a neutral context when Chinese language materials are used in the experiment? (3) Is SI generation costless or costly in terms of processing resources? (4) In line with the SI generation process, what conclusion can be made about the cognitive processing model of language meaning? Is it a parallel model or a linear model? Or is it a dynamic and hierarchical model? According to previous theoretical debates and experimental conflicts, presumptions could be made that SI, in Chinese language, might be generated in the upper-bound contexts. Besides, the response time might be faster in upper-bound than that found in lower-bound context. SI generation in neutral context might be the slowest. At last, a conclusion would be made that the processing model of SI could not be verified by either absolute structural or pragmatic approaches. It is, rather, a dynamic and complex processing mechanism, in which the interaction of language forms, ad hoc context, mental context, background knowledge, speakers’ interaction, etc. are involved.

Keywords: cognitive linguistics, pragmatics, scalar implicture, experimental study, Chinese language

Procedia PDF Downloads 353

Authors: Nadia Soledad Peralta

Abstract:

Within the framework of socio-cognitive interaction, argumentation is understood as a psychological process that supports and induces reasoning and learning. Most authors emphasize the great potential of argumentation to negotiate with contradictions and complex decisions. So argumentation is a target for researchers who highlight the importance of social and cognitive processes in learning. In the context of social interaction among university students, different types of arguments are analyzed according to group size (dyads and triads) and the type of task (reading of frequency tables, causal explanation of physical phenomena, the decision regarding moral dilemma situations, and causal explanation of social phenomena). Eighty-nine first-year social sciences students of the National University of Rosario participated. Two groups were formed from the results of a pre-test that ensured the heterogeneity of points of view between participants. Group 1 consisted of 56 participants (performance in dyads, total: 28), and group 2 was formed of 33 participants (performance in triads, total: 11). A quasi-experimental design was performed in which effects of the two variables (group size and type of task) on the argumentation were analyzed. Three types of argumentation are described: authentic dialogical argumentative resolutions, individualistic argumentative resolutions, and non-argumentative resolutions. The results indicate that individualistic arguments prevail in dyads. That is, although people express their own arguments, there is no authentic argumentative interaction. Given that, there are few reciprocal evaluations and counter-arguments in dyads. By contrast, the authentically dialogical argument prevails in triads, showing constant feedback between participants’ points of view. It was observed that, in general, the type of task generates specific types of argumentative interactions. However, it is possible to emphasize that the authentically dialogic arguments predominate in the logical tasks, whereas the individualists or pseudo-dialogical are more frequent in opinion tasks. Nerveless, these relationships between task type and argumentative mode are best clarified in an interactive analysis based on group size. Finally, it is important to stress the value of dialogical argumentation in educational domains. Argumentative function not only allows a metacognitive reflection about their own point of view but also allows people to benefit from exchanging points of view in interactive contexts.

Keywords: sociocognitive interaction, argumentation, university students, size of the grup

Procedia PDF Downloads 77

Authors: Bouchra Nadji, Elaid Bouchetob

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PV systems are common in residential and industrial settings because of their low, upfront costs and operating costs throughout their lifetimes. Buck or boost converters are used in photovoltaic systems, regardless of whether the system is autonomous or connected to the grid. These converters became less appealing because of their low efficiency, inadequate power density, and use of silicon for their power components. Traditional devices based on Si are getting close to reaching their theoretical performance limits, which makes it more challenging to improve the performance and efficiency of these devices. GaN and SiC are the two types of WBG semiconductors with the most recent technological advancements and are available. Tolerance to high temperatures and switching frequencies can reduce active and passive component size. Utilizing high-efficiency dc-dc boost converters is the primary emphasis of this work. These converters are for photovoltaic systems that use wave energy.

Keywords: component, Artificial intelligence, PV System, ANN MPPT, DC-DC converter

Procedia PDF Downloads 53

Authors: Muhammad Umair Siqqiqui

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The presented scheme in this work utilized the minimum number of grid nodes and provided higher-order accurate results, making it highly efficient and, at the same time, highly accurate. This concept is called compact schemes and has gained great popularity, which was the major target of this research work. Moreover, the salient features presented in this work are (a) the Development of a numerical scheme that involves a minimum number of stencil points, which means the implemented scheme is compact; (b) Development of a compact scheme that is highly accurate which means the implemented scheme is reliable and (c) Developing a highly efficient compact scheme means the implemented scheme is fast.

Keywords: minimum number of grid nodes, highly efficient, highly accurate, compact schemes

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Authors: Emmanuel Carlo Belara, Albert John Dela Merced, Mark Anthony Dominguez, Diomari Erasga, Jerome Ferrer, Bernard Ombrog

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Social distancing errors are a common prevalence between vaccinated and unvaccinated in the Filipino community. This study aims to identify and relate the factors on how they affect our daily lives. Observed factors include memory, attention, anxiety, decision-making, and stress. Upon applying the ergonomic tools and statistical treatment such as t-test and multiple linear regression, stress and attention turned out to have the most impact to the errors of social distancing.

Keywords: vaccinated, unvaccinated, socoal distancing, filipinos

Procedia PDF Downloads 194

Authors: Halim Djerroud, Arab Ali Cherif

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We assume that conscious functions are implemented automatically. In other words that consciousness as well as the non-consciousness aspect of human thought, planning, and perception, are produced by biologically adaptive algorithms. We propose that the mechanisms of consciousness can be produced using similar adaptive algorithms to those executed by the mechanism. In this paper, we propose a computational model of consciousness, the ”Global Abstraction Workspace” which is an internal environmental modelling perceived as a multi-agent system. This system is able to evolve and generate new data and processes as well as actions in the environment.

Keywords: artificial consciousness, cognitive architecture, global abstraction workspace, multi-agent system

Procedia PDF Downloads 336

Authors: Selman Selim Akyüz, Mete Kazaz

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Since The First Gulf War, USA, “Leader of The Free World” has been in trouble with terror. The USA created a complexity in The Middle East and paid the price with terrorist attacks in homeland. USA has made serious mistakes in terms of antiterrorism and fight against its supporters. Democrats have repaired damages caused by the Republican Party's management. Old methods about antiterrorism have been slowly abandoned. Hollywood, too, has played an important part in this war. Sometimes, Hollywood became an unquestioned patriot, sometimes it cried for the death of American Soldiers far away. In this study, messages in The Homeland, broadcast in the USA and a lot of countries around the world, are analyzed in terms of Washington’s foreign policy and position of the CIA in the fight against antiterrorism. The series reflect an orientalist viewpoint and has been criticized for offensive policy against the government. Homeland wanted to offer a perspective for the USA to be the “Leader of The Free World” again but with a liberal-democrat approach, dialogue and rational intelligence methods.

Keywords: antiterrorism, CIA, homeland, USA

Procedia PDF Downloads 356

Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Alexandra Roy

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Building on literature on cognitive matching and social categorization and using the correspondence testing method, we test the interaction effect of person characteristics (Gender with physical attractiveness) and job context (client contact, industry status, coworker contact). As expected, while findings show a strong impact of gender with beauty on hiring chances, job context characteristics have also a significant overall effect of this hiring outcome. Moreover, the rate of positive responses varies according some of the recruiter’s characteristics. Results are robust to various sensitivity checks. Implications of the results, limitations of the study, and directions for future research are discussed.

Keywords: correspondence testing, discrimination, hiring, physical attractiveness

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Authors: Clement Chimezie Aladi, Itamar Shabtai

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A review of the literature in the last few years reveals that little attention has been paid to technological affordances in e-learning designs. However, affordances are key to engaging students and enabling teachers to actualize learning goals. E-learning systems (software and artifacts) need to be designed in such a way that the features facilitate perceptions of the affordances with minimal cognition. This study aimed to fill this gap in the literature and encourage further research in this area. It provides guidelines for facilitating the perception of affordances in e-learning design and advances Technology Affordance and Constraints Theory by incorporating the affordance-based design process, the principles of multimedia learning, e-learning design philosophy, and emotional and cognitive affordances.

Keywords: e-learning, technology affrodances, affordance based design, e-learning design

Procedia PDF Downloads 54

Authors: Shola Abidemi Olabode

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The technological revolution over the years has impacted journalistic practice. As a matter of fact, journalistic practice has evolved alongside technologies of every generation transforming news and reporting, entertainment, and politics. Alongside these developments, the emergence of new kinds of risks and harms associated with generative AI has become rife with implications for media and journalism. Despite their numerous benefits for research and development, generative AI technologies like ChatGPT introduce new practical, ethical, and regulatory complexities in the practice of media and journalism. This paper presents a preliminary overview of the new kinds of challenges and issues for journalism and media practice in the era of generative AI, the implications for Nigeria, and invites a consideration of methods to mitigate the evolving complexity. It draws mainly on desk-based research underscoring the literature in both developed and developing non-western contexts as a contribution to knowledge.

Keywords: AI, journalism, media, online harms

Procedia PDF Downloads 73

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 115

Authors: Naser Zourikalatehsamad, Seyed Abdorreza Payambarpour, Ibrahim Alwashali, Zahra Abdolkarimi

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The paper aims to evaluate the effect of online advertising on consumer purchase behavior in Malaysian organizations. The paper has potential to extend and refine theory. A survey was distributed among Students of UTM university during the winter 2014 and 160 responses were collected. Regression analysis was used to test the hypothesized relationships of the model. Result shows that the predictors (cost saving factor, convenience factor and customized product or services) have positive impact on intention to continue seeking online advertising.

Keywords: consumer purchase, convenience, customized product, cost saving, customization, flow theory, mass communication, online advertising ads, online advertising measurement, online advertising mechanism, online intelligence system, self-confidence, willingness to purchase

Procedia PDF Downloads 471

Authors: L. Ramirez, E. Guillén, J. Sánchez

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Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: analytics, telemedicine, internet of things, cloud computing

Procedia PDF Downloads 318

Authors: Mehdi Damaliamiri

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Undoubtedly, the power of language is hardly a new topic; indeed, the persuasive power of language accompanied by ideology has long been recognized in different aspects of life. The two and a half thousand-year-old Bisitun inscriptions in Iran, proclaiming the victories of the Persian King, Darius, are considered by some historians to have been an early example of the use of propaganda. Added to this, the modern age is the true cradle of fully-fledged ideologies and the ongoing process of centrifugal ideologization. The most visible work on ideology today within the field of linguistics is “Critical Discourse Analysis” (CDA). The focus of CDA is on “uncovering injustice, inequality, taking sides with the powerless and suppressed” and making “mechanisms of manipulation, discrimination, demagogy, and propaganda explicit and transparent.” possible way of relating language to ideology is to propose that ideology and language are inextricably intertwined. From this perspective, language is always ideological, and ideology depends on the language. All language use involves ideology, and so ideology is ubiquitous – in our everyday encounters, as much as in the business of the struggle for power within and between the nation-states and social statuses. At the same time, ideology requires language. Its key characteristics – its power and pervasiveness, its mechanisms for continuity and for change – all come out of the inner organization of language. The two phenomena are homologous: they share the same evolutionary trajectory. To get a more robust portrait of the power and ideology, we need to examine its potential place in the structure, and consider how such structures pattern in terms of the functional elements which organize meanings in the clause. This is based on the belief that all grammatical, including syntactic, knowledge is stored mentally as constructions have become immensely popular. When the structure of the clause is taken into account, the power and ideology have a preference for Complement over Subject and Adjunct. The subject is a central interpersonal element in discourse: it is one of two elements that form the central interactive nub of a proposition. Conceptually, there are countless ways of construing a given event and linguistically, a variety of grammatical devices that are usually available as alternate means of coding a given conception, such as political crime and corruption. In the theory of construal, then, which, like transitivity in Halliday, makes options available, Cognitive Linguistics can offer a cognitive account of ideology in language, where ideology is made possible by the choices a language allows for representing the same material situation in different ways. The possibility of promoting alternative construals of the same reality means that any particular choice in representation is always ideologically constrained or motivated and indicates the perspective and interests of the text-producer.

Keywords: power, ideology, court, discourse

Procedia PDF Downloads 157

Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

Procedia PDF Downloads 109

Authors: Ron Dick, P. S. D. V. Prasadarao, Glenn Coltman

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Agenesis of the corpus callosum (ACC) is a failure to develop corpus callosum - the large bundle of fibers of the brain that connects the two cerebral hemispheres. It can occur as a partial or complete absence of the corpus callosum. In the general population, its estimated prevalence rate is 1 in 4000 and a wide range of genetic, infectious, vascular, and toxic causes have been attributed to this heterogeneous condition. The diagnosis of ACC is often achieved by neuroimaging procedures. Though persons with ACC can perform normally on intelligence tests they generally present with a range of neuropsychological and social deficits. The deficit profile is characterized by poor coordination of motor movements, slow reaction time, processing speed and, poor memory. Socially, they present with deficits in communication, language processing, the theory of mind, and interpersonal relationships. The present paper illustrates the role of neuropsychological assessment with implications to psychosocial management in a case of agenesis of the corpus callosum. Method: A 27-year old left handed Caucasian male with a history of ACC was self-referred for a neuropsychological assessment to assist him in his employment options. Parents noted significant difficulties with coordination and balance at an early age of 2-3 years and he was diagnosed with dyspraxia at the age of 14 years. History also indicated visual impairment, hypotonia, poor muscle coordination, and delayed development of motor milestones. MRI scan indicated agenesis of the corpus callosum with ventricular morphology, widely spaced parallel lateral ventricles and mild dilatation of the posterior horns; it also showed colpocephaly—a disproportionate enlargement of the occipital horns of the lateral ventricles which might be affecting his motor abilities and visual defects. The MRI scan ruled out other structural abnormalities or neonatal brain injury. At the time of assessment, the subject presented with such problems as poor coordination, slowed processing speed, poor organizational skills and time management, and difficulty with social cues and facial expressions. A comprehensive neuropsychological assessment was planned and conducted to assist in identifying the current neuropsychological profile to facilitate the formulation of a psychosocial and occupational rehabilitation programme. Results: General intellectual functioning was within the average range and his performance on memory-related tasks was adequate. Significant visuospatial and visuoconstructional deficits were evident across tests; constructional difficulties were seen in tasks such as copying a complex figure, building a tower and manipulating blocks. Poor visual scanning ability and visual motor speed were evident. Socially, the subject reported heightened social anxiety, difficulty in responding to cues in the social environment, and difficulty in developing intimate relationships. Conclusion: Persons with ACC are known to present with specific cognitive deficits and problems in social situations. Findings from the current neuropsychological assessment indicated significant visuospatial difficulties, poor visual scanning and problems in social interactions. His general intellectual functioning was within the average range. Based on the findings from the comprehensive neuropsychological assessment, a structured psychosocial rehabilitation programme was developed and recommended.

Keywords: agenesis, callosum, corpus, neuropsychology, psychosocial, rehabilitation

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Authors: Robin Devillers, Chantal van Dijk

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Children face ambiguities in everyday language use. Particularly ambiguity in pronoun resolution can be challenging, whereas adults can rapidly identify the antecedent of the mentioned pronoun. Two main factors underlie this process, namely the accessibility of the referent and the syntactic cues of the pronoun. After 200ms, adults have converged the accessibility and the syntactic constraints, while relieving cognitive effort by considering contextual cues. As children are still developing their cognitive capacity, they are not able yet to simultaneously assess and integrate accessibility, contextual cues and syntactic information. As such, they fail to identify the correct referent and possibly fixate more on the competitor in comparison to adults. In this study, Dutch while-clauses were used to investigate the interpretation of pronouns by children. The aim is to a) examine the extent to which 7-10 year old children are able to utilise discourse and syntactic information during online and offline sentence processing and b) analyse the contribution of individual factors, including age, working memory, condition and vocabulary. Adult and child participants are presented with filler-items and while-clauses, and the latter follows a particular structure: ‘Anna and Sophie are sitting in the library. While Anna is reading a book, she is taking a sip of water.’ This sentence illustrates the ambiguous situation, as it is unclear whether ‘she’ refers to Anna or Sophie. In the unambiguous situation, either Anna or Sophie would be substituted by a boy, such as ‘Peter’. The pronoun in the second sentence will unambiguously refer to one of the characters due to the syntactic constraints of the pronoun. Children’s and adults’ responses were measured by means of a visual world paradigm. This paradigm consisted of two characters, of which one was the referent (the target) and the other was the competitor. A sentence was presented and followed by a question, which required the participant to choose which character was the referent. Subsequently, this paradigm yields an online (fixations) and offline (accuracy) score. These findings will be analysed using Generalised Additive Mixed Models, which allow for a thorough estimation of the individual variables. These findings will contribute to the scientific literature in several ways; firstly, the use of while-clauses has not been studied much and it’s processing has not yet been identified. Moreover, online pronoun resolution has not been investigated much in both children and adults, and therefore, this study will contribute to adults and child’s pronoun resolution literature. Lastly, pronoun resolution has not been studied yet in Dutch and as such, this study adds to the languages

Keywords: pronouns, online language processing, Dutch, eye-tracking, first language acquisition, language development

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Authors: Allen A. Espinosa, Larkins A. Trinidad

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The present study investigated collegiate students’ problem solving strategies and misconceptions in solving stoichiometric problems and later on formulate a teaching framework from the result of the study. The study found out that the most prominent strategies among students are the mole method and the proportionality method, which are both algorithmic by nature. Misconception was also noted as some students rely on Avogadro’s number in converting between moles. It is suggested therefore that the teaching of stoichiometry should not be confined to demonstration. Students should be involved in the process of thinking of ways to solve the problem.

Keywords: stoichiometry, Svogadro’s number, mole method, proportionality method

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

Procedia PDF Downloads 418

Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

Procedia PDF Downloads 202

Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

Procedia PDF Downloads 127

Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

Procedia PDF Downloads 120

Authors: Lei Wang, Jiahao Zhou

Abstract:

The development of heterogeneous computing clusters provides a strong computility guarantee for large-scale workflows (e.g., scientific computing, artificial intelligence (AI), etc.). However, the tasks within large-scale workflows have also gradually become heterogeneous due to different demands on computing resources, which leads to the addition of a task resource-restricted constraint to the workflow scheduling problem on heterogeneous computing platforms. In this paper, we propose a heterogeneous constrained minimum makespan scheduling algorithm based on the idea of greedy strategy, which provides an efficient solution to the heterogeneous workflow scheduling problem in a heterogeneous platform. In this paper, we test the effectiveness of our proposed scheduling algorithm by randomly generating heterogeneous workflows with heterogeneous computing platform, and the experiments show that our method improves 15.2% over the state-of-the-art methods.

Keywords: heterogeneous computing, workflow scheduling, constrained resources, minimal makespan

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Authors: Mladen Milicevic

Abstract:

This paper takes a semiological approach to show, that the meaning is not located in the art object nor it is exclusively in the mind of the perceiver, but rather lies in the relationship of the two. The ultimate intention of making art is to be presented and perceived by subjective human beings. But there will be as many different interpretations of the art presented to them, as they are individuals in the audience. To support this claim, the latest research from neuroscience, cognitive psychology, and Neo-Darwinism is used. This paper draws on Richard Dawkins’ concept of memes as one of the main tools for explaining how differences get created within various socio-cultural environments. Analyzing pitfalls of the modernist worldview, the author proposes postmodern methods as more efficient ways of understanding today’s complexities in the art, culture, and the world. Deconstructing how these differences have come about, presents a possibility for the transgression of the opposing and many times adamant viewpoints.

Keywords: semiology, music, meme, postmodern

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Authors: Veronika Pivkina, Alla Kim, Khon Nataliya

Abstract:

Actuality of the present study is defined first of all the role of subjective well-being problem in modern psychology and the comprehending of subjective well-being by current students. Purpose of this research is to educe peculiarities of comprehending of subjective well-being by students with various levels of emotional intelligent. Methods of research are adapted Russian-Language questionnaire of K. Riff 'The scales of psychological well-being'; emotional intelligent questionnaire of D. V. Lusin. The research involved 72 student from different universities and disciplines aged between 18 and 24. Analyzing the results of the studies, it can be concluded that the understanding of happiness in different groups of students with high and low levels of overall emotional intelligence is different, as well as differentiated by gender. Students with higher level of happiness possess more capacity and higher need to control their emotions, to cause and maintain the desired emotions and control something undesirable.

Keywords: subjective well-being, emotional intelligent, psychology of comprehending, students

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Authors: Agata M. Kołodziejczyk, Mateusz Harasymczuk, Pierre-Yves Girardin, Lucie Davidová

Abstract:

Subjective time perception implies connection to cognitive functions, attention, memory and awareness, but a little is known about connections with homeostatic states of the body coordinated by circadian clock. In this paper, we present results from experimental study of subjective time perception in volunteers performing physical activity on treadmill in various phases of their circadian rhythms. Subjects were exposed to several time illusions simulated by programmed timing systems. This study brings better understanding for further improvement of of work quality in isolated areas. 

Keywords: biological clock, light, time illusions, treadmill

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Authors: Ayanda P. Deliwe, Storm B. Watson

Abstract:

The COVID-19 pandemic has disrupted the established practices of higher education institutions (HEIs). Most higher education institutions worldwide had to shift from traditional face-to-face to online learning. The online environment and new online tools are disrupting the way in which higher education is presented. Furthermore, the structures of higher education institutions have been impacted by rapid advancements in information and communication technologies. Emerging technologies should not be viewed in a negative light because, as opposed to the traditional curriculum that worked to create productive and efficient researchers, emerging technologies encourage creativity and innovation. Therefore, using technology together with traditional means will enhance teaching and learning. Emerging technologies in higher education not only change the experience of students, lecturers, and the content, but it is also influencing the attraction and retention of students. Higher education institutions are under immense pressure because not only are they competing locally and nationally, but emerging technologies also expand the competition internationally. Emerging technologies have eliminated border barriers, allowing students to study in the country of their choice regardless of where they are in the world. Higher education institutions are becoming indifferent as technology is finding its way into the lecture room day by day. Academics need to utilise technology at their disposal if they want to get through to their students. Academics are now competing for students' attention with social media platforms such as WhatsApp, Snapchat, Instagram, Facebook, TikTok, and others. This is posing a significant challenge to higher education institutions. It is, therefore, critical to pay attention to emerging technologies in order to see how they can be incorporated into the classroom in order to improve educational quality while remaining relevant in the work industry. This study aims to understand how emerging technologies have been utilised at Nelson Mandela University in presenting teaching and learning activities since April 2020. The primary objective of this study is to analyse how academics are incorporating emerging technologies in their teaching and learning activities. This primary objective was achieved by conducting a literature review on clarifying and conceptualising the emerging technologies being utilised by higher education institutions, reviewing and analysing the use of emerging technologies, and will further be investigated through an empirical analysis of the use of emerging technologies at Nelson Mandela University. Findings from the literature review revealed that emerging technology is impacting several key areas in higher education institutions, such as the attraction and retention of students, enhancement of teaching and learning, increase in global competition, elimination of border barriers, and highlighting the digital divide. The literature review further identified that learning management systems, open educational resources, learning analytics, and artificial intelligence are the most prevalent emerging technologies being used in higher education institutions. The identified emerging technologies will be further analysed through an empirical analysis to identify how they are being utilised at Nelson Mandela University.

Keywords: artificial intelligence, emerging technologies, learning analytics, learner management systems, open educational resources

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