Search results for: intercultural intelligence

Authors: P. J. Moore-Jones

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Emirati students studying at the University of the Emirates, one of three major public institutions of higher learning in the United Arab Emirates (UAE), have a wide demographic of faculty members teaching them an equally wide variety of courses. These faculty members bring with them their own cultural assumptions, methods, expectations, educational practices and use of language. The history of multiculturalism in the UAE coupled with the contemporary multiculturalism that exists in higher education Dubai create intriguing phenomena within the classroom. This study seeks to delve into students’ and faculty members’ perceptions of the social stratification that exist in this context. Data were collected via semi-structured interviews with both and analyzed from an interpretive perspective. Findings suggest the social stratification with is deeply-seeded in the multicultural history of the region and country are reflected in the everyday interworkings of education in modern day Dubai. The relevance of this research lies in that these findings can provide valuable insights into not only the attitudes and perceptions of these Emirati students might also be applicable to any of those student populations may exist.

Keywords: social stratification, intercultural competence, Dubai, United Arab Emirates

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Authors: Emine Akar

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The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

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The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter

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Authors: Soheila Sadeghi

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— The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages—such as improved efficiency, reduced bias, and hyper-personalization—it raises significant concerns about employee well-being, job security, fairness, and transparency. The study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Key findings reveal that: (a) while AI can enhance efficiency and reduce bias, it also raises concerns about job security, fairness, and privacy; (b) transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes; and (c) AI systems can both support and undermine employee well-being, depending on how they are implemented and perceived. The research introduces an AI-employee well-being Interaction Framework, illustrating how AI influences employee perceptions, behaviors, and outcomes. Organizational strategies, such as (a) clear communication, (b) upskilling programs, and (c) employee involvement in AI implementation, are identified as crucial for mitigating negative impacts and enhancing positive outcomes. The study concludes that the successful integration of AI in HR requires a balanced approach that (a) prioritizes employee well-being, (b) facilitates human-AI collaboration, and (c) ensures ethical and transparent AI practices alongside technological advancement.

Keywords: artificial intelligence, human resources, employee well-being, job satisfaction, organizational support, transparency in AI

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Authors: Eftychia Damaskou

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This article intends to place the question of the adequate approach for teaching multilingualism within the public education. Linguistic education, as it is defined by the Common European Framework of Reference for the Languages, is no longer the proficiency in one or two languages. It’s about the development of a linguistic repertoire, where all linguistic skills find their place. In fact, the linguistic theories that frame the development of plurilingual competence point out the affective and intercultural aspect of such a process, insisting on an awareness of linguistic diversification, rather than an acquisition of communicative competence in many languages. In this spirit, our article attempts to go beyond a mere plurilingual awareness, present a research based on an experience in class, within 115 pupils, aiming at the development of plurilingual competence in five unknown foreign languages. This experience was held through a teaching unit personally conceived and applied, and consisted of a series of 6 activities based on a cross-linguistic content approach. The data analysis proves to be very interesting, as it reveals the development of plurilingual competences, as well as positive attitudes towards less common languages by the majority of our sample.

Keywords: multilingual awareness, multilingual teaching material, plurilingual competence

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Authors: Angelyn Del Mundo

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Bullying cases are a proliferating issue that schools need to address. This calls for a challenge in providing effective measures to reduce bullying. The study aimed to determine which among the socio-emotional aspects of adolescent males could predict bullying. The respondents of the study were the grades 10 and 11 level and the selection of the respondents was based on the names listed by the teachers and guidance counselors through the Student Nomination Questionnaire. The Bullying Survey Questionnaire Checklist was answered by the respondents to be able to identify their most observed bullying behavior. On the other hand, the level of their mental ability was measured through the use of Otis-Lennon School Ability Test, while their socio-emotional aspects was is classified into 2 contexts: emotional intelligence and personality traits which were determined with the use of Bar-On Emotional Quotient Inventory: Youth Version (BarOn EQ-i:YV) and the Five-Factor Personality Inventory-Children (FFPI-C). Results indicated that majority of the respondents have average level of mental ability and socio-emotional aspects. However, many students have low to markedly low level interpersonal scale. Furthermore, general mood and emotional regulation were found as predictors of bullying behaviors. These findings became the basis for a proposed bullying intervention program.

Keywords: bullying, emotional intelligence, mental ability, personality traits

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Authors: Ngoc T. Bui

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The key issue for teachers of a modern foreign language is the creation of a pedagogic environment, and this means that an understanding of context is vital. A pedagogic environment addresses the following: time, feedback, relations with other people, curriculum integration, forms of knowledge, resources and control in the pedagogic relationship. In this light, the multiple case study of the implementation of a modern foreign language curriculum focuses on exploring Vietnamese contexts and participants’ perceptions of factors that may affect their implementation process in order to examine thoroughly how the communicative language teaching (CLT) curriculum is being implemented in second language classrooms. A mixed methods approach is utilized to investigate contextual and personal factors that may affect teachers’ implementation of curriculum and pedagogical reform in Vietnam. This project therefore has the capability to inform stakeholders of useful information and identify further changes and measures to solve potential problems to ensure the achievement of the curriculum goals. The expected outcomes may also lead to intercultural language teaching guidelines to support english as a foreign language (EFL) teachers with curriculum design, planning and how to create pedagogic environment to best implement it.

Keywords: communicative language teaching, context, curriculum implementation, modern foreign language, pedagogic environment

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Authors: Mandeep Saini

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The rapid advancement of Artificial Intelligence (AI) is transforming various fields, including intellectual property law. This paper explores the emerging role of AI in interpreting patent claims, a critical and highly specialized area within intellectual property rights. Patent claims define the scope of legal protection granted to an invention, and their precise interpretation is crucial in determining the boundaries of the patent holder's rights. Traditionally, this interpretation has relied heavily on the expertise of patent examiners, legal professionals, and judges. However, the increasing complexity of modern inventions, especially in fields like biotechnology, software, and electronics, poses significant challenges to human interpretation. Introducing AI into patent claim interpretation raises several legal and ethical concerns. This paper addresses critical issues such as the reliability of AI-driven interpretations, the potential for algorithmic bias, and the lack of transparency in AI decision-making processes. It considers the legal implications of relying on AI, particularly regarding accountability for errors and the potential challenges to AI interpretations in court. The paper includes a comparative study of AI-driven patent claim interpretations versus human interpretations across different jurisdictions to provide a comprehensive analysis. This comparison highlights the variations in legal standards and practices, offering insights into how AI could impact the harmonization of international patent laws. The paper proposes policy recommendations for the responsible use of AI in patent law. It suggests legal frameworks that ensure AI tools complement, rather than replace, human expertise in patent claim interpretation. These recommendations aim to balance the benefits of AI with the need for maintaining trust, transparency, and fairness in the legal process. By addressing these critical issues, this research contributes to the ongoing discourse on integrating AI into the legal field, specifically within intellectual property rights. It provides a forward-looking perspective on how AI could reshape patent law, offering both opportunities for innovation and challenges that must be carefully managed to protect the integrity of the legal system.

Keywords: artificial intelligence (ai), patent claim interpretation, intellectual property rights, algorithmic bias, natural language processing, patent law harmonization, legal ethics

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Authors: Xing Lyu

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Multimodal metaphor promotes the further development and refinement of multimodal discourse study. Cultural aspects matter a lot not only in creating but also in comprehending multimodal metaphor. By analyzing the target domain and the source domain in 10 public service advertisements of China and Germany about environmental protection, this paper compares the source when the target is alike in each multimodal metaphor in order to seek similarities and differences across cultures. The findings are as follows: first, the multimodal metaphors center around three major topics: the earth crisis, consequences of environmental damage, and appeal for environmental protection; second, the multimodal metaphors mainly grounded in three universal conceptual metaphors which focused on high level is up; earth is mother and all lives are precious. However, there are five Chinese culture-specific multimodal metaphors which are not discovered in Germany ads: east is high leve; a purposeful life is a journey; a nation is a person; good is clean, and water is mother. Since metaphors are excellent instruments on studying ideology, this study can be helpful on intercultural/cross-cultural communication.

Keywords: multimodal metaphor, cultural aspects, public service advertising, cross-cultural communication

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Authors: Sonia Anand Knowlton

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As the use of Artificial Intelligence is becoming increasingly integrated in virtually every area of life, the need and interest to logically formalize the law and judicial reasoning is growing tremendously. The study of argumentation frameworks (AFs) provides promise in this respect. AF’s provide a way of structuring human reasoning using a formal system of non-monotonic logic. P.M. Dung first introduced this framework and demonstrated that certain arguments must prevail and certain arguments must perish based on whether they are logically “attacked” by other arguments. Dung labelled the set of prevailing arguments as the “preferred extension” of the given argumentation framework. Trevor Bench-Capon’s Value-based Argumentation Frameworks extended Dung’s AF system by allowing arguments to derive their force from the promotion of “preferred” values. In VAF systems, the success of an attack from argument A to argument B (i.e., the triumph of argument A) requires that argument B does not promote a value that is preferred to argument A. There has been thorough discussion of the application of VAFs to the law within the computer science literature, mainly demonstrating that legal cases can be effectively mapped out using VAFs. This article analyses VAFs from a jurisprudential standpoint to provide a philosophical and theoretical analysis of what VAFs tell the legal community about the judicial reasoning, specifically distinguishing between legal and moral reasoning. It highlights the limitations of using VAFs to account for judicial moral reasoning in theory and in practice.

Keywords: nonmonotonic logic, legal formalization, computer science, artificial intelligence, morality

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Authors: Guenther Schuh, Christina Koenig, Nico Schoen, Markus Wellensiek

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Identification and selection of appropriate product and manufacturing technologies are key factors for competitiveness and market success of technology-based companies. Therefore many companies perform technology intelligence (TI) activities to ensure the identification of evolving technologies at the right time. Technology monitoring is one of the three base activities of TI, besides scanning and scouting. As the technological progress is accelerating, more and more technologies are being developed. Against the background of limited resources it is therefore necessary to focus TI activities. In this paper, we propose a concept for defining appropriate search fields for technology monitoring. This limitation of search space leads to more concentrated monitoring activities. The concept will be introduced and demonstrated through an anonymized case study conducted within an industry project at the Fraunhofer Institute for Production Technology. The described concept provides a customized monitoring approach, which is suitable for use in technology-oriented companies especially those that have not yet defined an explicit technology strategy. It is shown in this paper that the definition of search fields and search tasks are suitable methods to define topics of interest and thus to direct monitoring activities. Current as well as planned product, production and material technologies as well as existing skills, capabilities and resources form the basis of the described derivation of relevant search areas. To further improve the concept of technology monitoring the proposed concept should be extended during future research e.g. by the definition of relevant monitoring parameters.

Keywords: monitoring radar, search field, technology intelligence, technology monitoring

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Authors: Waleed Afandi

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Artificial intelligence (AI) has rapidly transformed various sectors, including education. This paper explores the integration of AI in education, emphasizing its potential to revolutionize learning processes, enhance teaching methodologies, and personalize education. We examine the historical context of AI in education, current applications, and the potential challenges and ethical considerations associated with its implementation. By reviewing a wide range of literature, this study aims to provide a comprehensive understanding of how AI can be leveraged to improve educational outcomes and the future directions of AI-driven educational innovations. Additionally, the paper discusses the impact of AI on student engagement, teacher support, and administrative efficiency. Case studies highlighting successful AI applications in diverse educational settings are presented, showcasing the practical benefits and real-world implications. The analysis also addresses potential disparities in access to AI technologies and suggests strategies to ensure equitable implementation. Through a balanced examination of the promises and pitfalls of AI in education, this study seeks to inform educators, policymakers, and technologists about the optimal pathways for integrating AI to foster an inclusive, effective, and innovative educational environment.

Keywords: artificial intelligence, education, personalized learning, teaching methodologies, educational outcomes, AI applications, student engagement, teacher support, administrative efficiency, equity in education

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Sidika Di̇lşad Kaya, Ahmet Seli̇m Kaya, Ibrahi̇m Eri̇k, Havva Yaldiz, Yalçin Kaya

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The aim of this study was to reveal the effects of right brain development on reading, comprehension, learning and concentration levels and rapid processing skills in students with low vision and students with standard development, and to explore the effects of right and left brain integration on students' academic success and the permanence of the learned knowledge. A total of 68 students with a mean age of 10.01±0.12 were included in the study, 58 of them with standard development, 9 partially visually impaired and 1 totally visually disabled student. The student with a total visual impairment could not participate in the reading speed test due to her total visual impairment. The following data were measured in the participant students before the project; Reading speed measurement in 1 minute, Reading comprehension questions, Burdon attention test, 50 questions of math quiz timed with a stopwatch. Participants were trained for 3 weeks, 5 days a week, for a total of two hours a day. In this study, right-brain developing exercises were carried out with the use of an abacus, and it was aimed to develop both mathematical and attention of students with questions prepared with numerical data taken from fairy tale activities. Among these problems, the study was supported with multiple-choice, 5W (what, where, who, why, when?), 1H (how?) questions along with true-false and fill-in-the-blank activities. By using memory cards, students' short-term memories were strengthened, photographic memory studies were conducted and their visual intelligence was supported. Auditory intelligence was supported by aiming to make calculations by using the abacus in the minds of the students with the numbers given aurally. When calculating the numbers by touching the real abacus, the development of students' tactile intelligence is enhanced. Research findings were analyzed in SPSS program, Kolmogorov Smirnov test was used for normality analysis. Since the variables did not show normal distribution, Wilcoxon test, one of the non-parametric tests, was used to compare the dependent groups. Statistical significance level was accepted as 0.05. The reading speed of the participants was 83.54±33.03 in the pre-test and 116.25±38.49 in the post-test. Narration pre-test 69.71±25.04 post-test 97.06±6.70; BURDON pretest 84.46±14.35 posttest 95.75±5.67; rapid math processing skills pretest 90.65±10.93, posttest 98.18±2.63 (P<0.05). It was determined that the pre-test and post-test averages of students with typical development and students with low vision were also significant for all four values (p<0.05). As a result of the data obtained from the participants, it is seen that the study was effective in terms of measurement parameters, and the findings were statistically significant. Therefore, it is recommended to use the method widely.

Keywords: Abacus, reading speed, multiple intelligences, right brain training, visually impaired

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Authors: Azman Ramlie, Vivian Foo Jing Wen

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As the advancement of technology has tremendously changed the way people communicate with each other, it has opened up opportunities for interracial relationships. Interracial relationship is also known as intercultural or interethnic relationships. This research topic aims to study on students’ perceptions toward interracial relationship in terms of their level of acceptance and approval. In addition, the study also focuses on determining and ranking the factors that affects students’ perceptions towards interracial relationship. This study also targets to determine the differences in students’ perceptions from business and communication major. This study uses a sample of university students from Sunway University, particularly on students in business and communication major. The study was conducted through online survey questionnaires. Results revealed that students’ level of acceptance towards interracial relationship was high. In addition, results also further indicated that family members’ support was one of the most important factors in influencing students’ perception towards interracial relationship. No diverse differences of perceptions among students in business and communication major towards interracial relationship were shown in the findings. Most of the result showed that both majors turned out to have similar perceptions towards interracial relationship. It can be said that the students’ perception towards interracial relationship did not differ from which major the students was in but their family background that would shape their perceptions.

Keywords: interracial relationship, racial, relationship, perceptions

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

Abstract:

The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: Ahmad Haidar

Abstract:

Modern economic systems are characterized by growing complexity, and addressing their challenges requires innovative approaches. This study examines the implications of artificial intelligence (AI) on unemployment in Europe from a macroeconomic perspective, employing data modeling techniques to understand the relationship between AI integration and labor market dynamics. To understand the AI-unemployment nexus comprehensively, this research considers factors such as sector-specific AI adoption, skill requirements, workforce demographics, and geographical disparities. The study utilizes a panel data model, incorporating data from European countries over the last two decades, to explore the potential short-term and long-term effects of AI implementation on unemployment rates. In addition to investigating the direct impact of AI on unemployment, the study also delves into the potential indirect effects and spillover consequences. It considers how AI-driven productivity improvements and cost reductions might influence economic growth and, in turn, labor market outcomes. Furthermore, it assesses the potential for AI-induced changes in industrial structures to affect job displacement and creation. The research also highlights the importance of policy responses in mitigating potential negative consequences of AI adoption on unemployment. It emphasizes the need for targeted interventions such as skill development programs, labor market regulations, and social safety nets to enable a smooth transition for workers affected by AI-related job displacement. Additionally, the study explores the potential role of AI in informing and transforming policy-making to ensure more effective and agile responses to labor market challenges. In conclusion, this study provides a comprehensive analysis of the macroeconomic implications of AI on unemployment in Europe, highlighting the importance of understanding the nuanced relationships between AI adoption, economic growth, and labor market outcomes. By shedding light on these relationships, the study contributes valuable insights for policymakers, educators, and researchers, enabling them to make informed decisions in navigating the complex landscape of AI-driven economic transformation.

Keywords: artificial intelligence, unemployment, macroeconomic analysis, european labor market

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Authors: Yuval Reinfeld

Abstract:

This paper explores the European Union’s expanding influence as a global regulatory power, particularly in the realms of legal, security, and privacy challenges within the digital landscape. As digital regulation becomes increasingly vital, the EU has positioned itself as a leading exporter of privacy and cybersecurity standards through landmark frameworks like the General Data Protection Regulation (GDPR), the Artificial Intelligence Act (AIA), and the Digital Services Act (DSA). These regulations have set global benchmarks, extending their influence well beyond Europe’s borders by shaping legal frameworks in third countries and guiding the development of global digital governance. Central to this regulatory diffusion is the European Court of Justice (CJEU), whose rulings consistently reinforce and extend the reach of EU standards on an international scale. Through mechanisms such as trade agreements, adequacy decisions, and multilateral cooperation, the EU has constructed a regulatory ecosystem that other jurisdictions increasingly adopt. This paper investigates key CJEU cases to illustrate how the EU’s legal instruments in privacy, security, and AI contribute to its role as a global standard-setter. By examining the intersection of digital governance, international law, and normative power, this research provides a thorough analysis of the EU’s regulatory impact on global privacy, cybersecurity, and AI frameworks.

Keywords: digital privacy, cybersecurity, GDPR, European Union Law, artificial intelligence, global normative power

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Authors: Zerroug Abdelhamid, Danielle Chassoux

Abstract:

Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods.

Keywords: simulation, cancerous tumor, Markov fields, fractal dimension, extraction, recovering

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Authors: Ke Qiu, Minzi Mao, Jianjun Ren, Yu Zhao

Abstract:

Although much has been done to investigate the influence of obstructive sleep apnea (OSA) on cognitive function, little attention has been paid to the role which gender differences play in this association. In the present study, we aim to explore the gender-specific association between OSA and cognitive impairment. Participants from UK biobank who have completed at least one of the five baseline cognitive tests (visuospatial memory, prospective memory, fluid intelligence, short numeric memory and reaction time) were included and were further categorized into three groups: (1) OSA, (2) self-reported snoring but without OSA, and (3) healthy controls (without OSA or snoring). Multivariable regression analysis was performed to examine the associations among snoring, OSA and performance of each of the five cognitive domains. A total of 267,889 participants (47% male, mean age: 57 years old) were included in our study. In the multivariable regression analysis, female participants in the OSA group had a higher risk of having poor prospective memory (OR: 1.24, 95% CI: 1.02～1.50, p = 0.03). Meanwhile, among female participants, OSA were inversely associated with the performances of fluid intelligence (β: -0.29, 95% CI: -0.46～-0.13, p < 0.001) and short-numeric memory (β: -0.14, 95% CI: -0.35～0.08, p = 0.02). In contrast, among male participants, no significant association was observed between OSA and impairment of the five cognitive domains. Overall, OSA was significantly associated with cognitive impairment in female participants rather than in male participants, indicating that more special attention and timely interventions should be given to female OSA patients to prevent further cognitive impairment.

Keywords: obstructive sleep apnea (OSA), cognitive impairment, gender-specific association, UK biobank

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Authors: Boumaraf Malak

Abstract:

The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

Procedia PDF Downloads 85