Search results for: cognitive artificial intelligence

Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: Tarek Aboueldahab, Hanan Farag

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Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization.

Keywords: cost minimization, multi-vehicle routing problem, passive congregation, discrete swarm, passive congregation

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Authors: I. A. Farhat

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The dynamic economic dispatch (DED) problem is one of the complex, constrained optimization problems that have nonlinear, con-convex and non-smooth objective functions. The purpose of the DED is to determine the optimal economic operation of the committed units while meeting the load demand. Associated to this constrained problem there exist highly nonlinear and non-convex practical constraints to be satisfied. Therefore, classical and derivative-based methods are likely not to converge to an optimal or near optimal solution to such a dynamic and large-scale problem. In this paper, an Artificial Immune System technique (AIS) is implemented and applied to solve the DED problem considering the transmission power losses and the valve-point effects in addition to the other operational constraints. To demonstrate the effectiveness of the proposed technique, two case studies are considered. The results obtained using the AIS are compared to those obtained by other methods reported in the literature and found better.

Keywords: artificial immune system, dynamic economic dispatch, optimal economic operation, large-scale problem

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Authors: Tünde Tifordiána Simonyi, Tímea Harmath-Tánczos

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The primary aim of the current research is to present the significance of a multicultural perspective in clinical neuropsychology and to present the test battery of the Multicultural Neuropsychological Scale (MUNS). The method includes the MUNS screening tool that involves stimuli common to most cultures in the world. The test battery measures general cognitive functioning focusing on five cognitive domains (memory, executive function, language, visual construction, and attention) tested with seven subtests that can be utilized within a wide age range (15-89), and lower and higher education participants. It is a scale that is sensitive to mild cognitive impairments. Our study presents the first results with the Hungarian translation of MUNS on a healthy sample. The education range was 4-25 years of schooling. The Hungarian sample was recruited by snowball sampling. Within the investigated population (N=151) the age curve follows an inverted U-shaped curve regarding cognitive performance with a high load on memory. Age, reading fluency, and years of education significantly influenced test scores. The sample was tested twice within a 14-49 days interval to determine test-retest reliability, which is satisfactory. Besides the findings of the study and the introduction of the test battery, the article also highlights its potential benefits for both research and clinical neuropsychological practice. The importance of adapting, validating and standardizing the test in other languages besides the Hungarian language context is also stressed. This test battery could serve as a helpful tool in mapping general cognitive functions in psychiatric and neurological disorders regardless of the cultural background of the patients.

Keywords: general cognitive functioning, multicultural, MUNS, neuropsychological test battery

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Authors: Meghry Nazarian, Ibrahim Duyar

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Effective management of a diverse workforce in the United Arab Emirates (UAE) presents peculiar importance as two-thirds of residents are expatriates who have diverse ethnic and cultural backgrounds. Like any other organization in the country, UAE schools have become upmost diverse settings in the world. The purpose of this study was to examine whether principals’ cultural intelligence has direct and indirect (moderating) influences on teachers’ perceived diversity climate. A quantitative causal-comparative research design was employed to analyze the data. Participants included random samples of principals and teachers working in the private and charter schools in the Emirate of Abu Dhabi. The data-gathering online questionnaires included previously developed and validated scales as the measures of study variables. More specifically, the multidimensional short-form measure of Cultural Intelligence (CQ) and the diversity climate scale were used to measure the study variables. Multivariate statistics, including the analysis of multivariate analysis of variance (MANCOVA) and structural equation modeling (SEM), were employed to examine the relationships between the study variables. The preliminary analyses of data showed that principals and teachers have differing views of diversity management and climate in schools. Findings also showed that principals’ cultural intelligence has both direct and moderating influences on teachers’ perceived diversity climate. The study findings are expected to inform policymakers and practicing educational leaders in addressing diversity management in a country where the majority of the residents are the minority who have diverse ethnic and cultural backgrounds.

Keywords: diversity management, united arab emirates, school principals’ cultural intelligence (CQ), teachers’ perceived diversity climate

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Authors: Monir Ahmed

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The main purpose of this paper is to analyze existing conceptual papers on the divine relationship. The primary objective of the paper is to illustrate cognitive orientation as a determinant of divine relationship. A further aim of the paper is to establish whether spiritual or religious practices, rituals, or acts alone could confirm a relationship with the sacred or the divine. Searching for the sacred or the divine is known to be a novel way of understanding the meaning and purpose of human existence, including the existence of everything around us. Inevitably, searching for the sacred provides an opportunity for human beings to form a relationship with the divine. Research suggests that discovering meaning and purpose through searching for the sacred or forming relationship with the divine enhances psychological well-being and eventually helps individuals to flourish. The search for the sacred and the discovery of the divine relationship thus have become interesting areas of study in Psychology of Religion and Spirituality. The existing conceptual papers on the relationship with the transcendent source, i.e., the divine creator, were systematically reviewed and analyzed. The outcome of the review reveals that the existing understanding of the relationship with the divine source is inadequate and that such understanding is unable to indicate or confirm a relationship with psychological well-being, including spiritual well-being. The importance of cognitive orientation, including cognitive processes as well as ‘creatio ex nihilo’ doctrine in searching for the sacred, is indicated. The author of this paper proposes that cognitive-theological understanding involving faith and belief about the creation and the divine source, the transcendent God is likely to offer a comprehensive understanding of the divine relationship.

Keywords: divine, well-being, analysis, cognitive orientation, ‘creatio ex nihilo’ doctrine

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Authors: Michael Saad Thabet Azrek

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Experiential advertising method is an unforgettable revel that remains deeply anchored within the customer's memory. Furthermore, client pleasure is defined as the emotional reaction to the stories provided that relate to precise products or services bought. Consequently, experiential advertising sports can influence the extent of consumer pleasure and loyalty. In this context, they have a look at pursuits to observe the connection between experiential advertising, purchaser satisfaction and loyalty to splendor merchandise in Konya. The outcomes of this examination confirmed that experiential marketing is an important indicator of consumer pride and loyalty, and that experiential advertising and marketing have a large positive impact on patron 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.

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Authors: Jawaher Bakheet Almudarra

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The Saudi Arabian educational system shared the philosophical principles, in its foundation, which concentrated on the achievement of goals, thereby taking up authoritative styles of leadership. However, organisations are beginning to be more liberal in today’s environment than in the 1940s and 1950s, and appealing to emotional intelligence as a tool and skill are needed for effective leadership. In the Saudi Arabian case, such developments are characterised by changes such as that of the educational supervisor having the role redefined to that of a director. This review tracks several parts: the first section helps western reader to understand the subtleties, complexities, and intricacies of the Saudi Arabia education system and its approach to leadership system of education, history, culture and political contribution. This can lead to the larger extent understand if emotional intelligence is a provocation for better leadership of Saudi Arabian education sector or not. The second part is the growth of educational supervision in Saudi Arabia, focusing on the education system, and evaluates the impact of emotional intelligence as a necessary skill in leadership. The third section looks at emotions and emotional intelligence, gender roles, and contributions by emotional intelligence in the education system. The education system of Saudi Arabia has undergone significant transformation. To fully understand the current climate of Saudi Arabia, it is essential to review this process of transformation in terms of the historical, cultural, political and social positions and transformations. Over the years, the education system in Saudi Arabia has undergone significant metamorphosis. The Saudi government has instituted a wide range of reforms in an attempt to improve education standards and outcomes, facilitate improvements and ensure that high standards of education standards are upheld to keep pace with the global environment and knowledge economy. Leadership itself has become an increasingly prominent aspect of educational reform worldwide. Emotional intelligence is often considered a significant aspect of leadership, but it is in its early stages in Saudi Arabia. Its recognition and adoption may improve leadership practices, particularly among educational supervisors and contribute to national and international understandings of leadership in Saudi Arabia. Studying leadership in the Saudi Arabian context is imperative as the new generation of leaders need to cultivate pertinent skills that will allow them to become fundamentally and positively involved in the regions’ decision making processes in order to impact the progression of the Saudi Arabian education system. Understanding leadership in the education context will allow for suitable inculcation of leadership skills. These skills include goal-setting, sound decision-making as well as problem-solving within the education system of Saudi Arabia.

Keywords: educational supervision, educational administration, emotional intelligence, educational leadership

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Authors: Sonda Jallouli, Sameh Ghroubi, Salma Sakka, Abdelmoneem Yahia, Mohamed Habib Elleuch, Imen Ben Dhia, Chokri Mhiri, Omar Hammouda

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The pathophysiology, prevalence, and progression of MS are gender dependent. Indeed, the inflammation is more pronounced in women, but the neurodegeneration is more important in men. In addition, women have more sleep disorders while men suffer more from cognitive decline. These non-physical disorders can negatively affect postural balance and fall risk. However, no study has examined the difference between men and women in those physical parameters in MS. Our objective was to determine the effect gender difference on postural balance and fall risk in MS persons. Methods: Eight men and twelve women with relapsing remitting-MS participated in this study. The assessment includes a posturographic examination to assess static (with eyes opened (EO) and eyes closed (EC)) and dynamic (with EO) postural balance. Unipedal balance and fall risk were assessed by a clinical unipedal balance test and the Four Square Step Test, respectively. Sleep quality was assessed using Spiegel's questionnaire, and cognitive assessment was performed using the Montreal Cognitive Assessment (MoCA) and the Simple Reaction Time Test. Results: Compared to men, women showed an increase in CdPVm in static bipedal condition with EC (p=0.037; d=0.71) and a decrease in MoCA scores (p=0.028; d=1.06). No gender differences were found in the other tests. Discussion: Static postural balance was more impaired in women compared to men. This result could be explained by the more pronounced cognitive decline observed in women compared to men. Indeed, cognitive disorders have been shown to be predictive factors of postural balance impairment. Conclusion: women were less stable than men in the static condition, possibly due to their lower cognitive performance. This gender difference could be taken into account by therapists in training programs.

Keywords: multiple sclerosis, bipedal postural balance, fall risk, sleep disturbance, cognitive deficiency

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Authors: Parisa Gharibi Khoshkar

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The global outbreak of Covid-19 forced a swift shift in the education sector, transitioning from traditional in-person settings to remote online setups in a short period. This abrupt change, coupled with health risks and other stressors such as the lack of social interaction, has had a negative impact on academic staff, leading to increased job-related stress and psychological pressures that can result in burnout. To address this, the current research aims to investigate the relationship between job stress and burnout among academic staff in Hebron, Palestine. Furthermore, this study examines the moderating role of emotional intelligence to gain a deeper understanding of its effects in reducing burnout among academic staff and teachers. This research posits that emotional intelligence plays a vital role in helping individuals manage job-related stress and anxiety, thereby preventing burnout. Using a self-administered questionnaire, the study gathered data from 185 samples comprising teachers and administrative staff from two universities in Hebron. The data was analyzed using moderated regression analysis, ANOVA model, and interaction plots. The findings indicate that work-related stress has a direct and significant influence on burnout. Moreover, the current results highlight that emotional intelligence serves as a key determinant in managing the negative effects of the pandemic-induced stress that can lead to burnout among individuals. Given the high-demand nature of the education sector, this research strongly recommends that school authorities take proactive measures to provide much-needed support to academic staff, enabling them to better cope with job stress and fostering an environment that prioritizes individuals' wellbeing. The results of this study hold practical implications for both scholars and practitioners, as they highlight the importance of emotional intelligence in managing stress and anxiety effectively. Understanding the significance of emotional intelligence can aid in implementing targeted interventions and support systems to promote the well-being and resilience of academic staff amidst challenging circumstances.

Keywords: job stress, burnout, employee wellbeing, emotional intelligence, industrial organizational psychology, human resource management, organizational psychology

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Authors: Shenouda Farag Aziz Ibrahim

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The relationship between terrorism and human rights has become an important issue in the fight against terrorism worldwide. This is based on the fact that terrorism and human rights are closely linked, so that when the former begins, the latter suffers. This direct link was recognized in the Vienna Declaration and Program of Action adopted by the International Conference on Human Rights held in Vienna on 25 June 1993, which recognized that terrorist acts aim to violate human rights in all their forms and manifestations. . Therefore, terrorism represents an attack on fundamental human rights. For this purpose, the first part of this article focuses on the relationship between terrorism and human rights and aims to show the relationship between these two concepts. In the second part, the concept of cyber threat and its manifestations are discussed. An analysis of the fight against terrorism in the context of human rights was also made..

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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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 behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral 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, sustainable development

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Anna Kristen

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There has been a pronounced increase in societal discourses around adolescent self-harm, yet there is a paucity of literature examining adolescent talk about self-harm that accounts for the sociocultural context. The objective of this study was to explore how adolescents with Depression talk about their self-harm engagement in consideration of both socio-cultural discourses and the therapy context during Cognitive-Behavioural Therapy (CBT) sessions. Utilizing a sample from the Improving Mood with Psychoanalytic and Cognitive Therapies study, discourse analysis was carried out on audio-recorded CBT sessions. The study established three groupings of results: (a) adolescent positioning as stuck in self-harm engagement; (b) adolescent positioning as ambivalent in the talk about ceasing self-harm; and (c) adolescent use of stigma discourses in self-harm talk & constructions of self-harm scars. These findings indicate that clinician awareness of adolescent use of language and discourse may inform interventions beyond Manualized CBT strategies. These findings are highly relevant in light of research that demonstrates CBT treatment for adolescent depression does not effectively address concurring self-harm and given that self-harm is the most significant risk factor predictive of subsequent suicidal behaviours.

Keywords: adolescence, cognitive-behavioral therapy, discourse, self-harm, stigma

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Sameil Hanna Samweil Botros

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The variation of social networking websites inside higher training has garnered enormous hobby in recent years, with numerous researchers thinking about it as a possible shift from the conventional lecture room-based learning paradigm. However, this boom in research and carried out research, but the adaption of SNS-based modules has not proliferated inside universities. This paper commences its contribution with the aid of studying the numerous fashions and theories proposed in the literature and amalgamates together various effective aspects for the inclusion of social technology within e-gaining knowledge. A three-phased framework is similarly proposed, which informs the important concerns for the hit edition of SNS in improving the student's mastering experience. This suggestion outlines the theoretical foundations as a way to be analyzed in sensible implementation across worldwide university campuses.

Keywords: eLearning, institutionalization, teaching and learning, transformation vtuber, ray tracing, avatar agriculture, adaptive, e-learning, technology eLearning, higher education, social network sites, student learning

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Authors: Amira Bousselmi

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This article presents a PIFA antenna designed for geolocation applications (GNSS) operating on 1.278 GHz, 2.8 GHz, 5.7 GHz and 10 GHz. To improve the performance of the antenna, an artificial magnetic conductor structure (AMC) was used. Adding the antenna with AMC resulted in a measured gain of 4.78 dBi. The results of simulations and measurements are presented. CST Microwave Studio is used to design and compare antenna performance. An antenna design methodology, design and characterization of the AMC surface are described as well as the simulated and measured performances of the AMC antenna are then discussed. Finally, in Section V, there is a conclusion.

Keywords: antenna multiband, global navigation system, AMC, Galeleo

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Authors: Safayat Ali Shaikh

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Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Joshefina Bengoechea Fernandez, Alex Bell

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In its 2021 guidance for policy makers, the UNESCO has proposed 4 areas where AI can be applied in educational settings: These are: 1) Education management and delivery; 2) Learning and assessment; 3) Empowering teachers and facilitating teaching, and 4) Providing lifelong learning possibilities (UNESCO, 2021). Like with wblockchain technologies, AI will automate the management of educational institutions. These include, but are not limited to admissions, timetables, attendance, and homework monitoring. Furthermore, AI will be used to select relevant learning content across learning platforms for each student, based on his or her personalized needs. A problem educators face is the “one-size-fits-all” approach that does not work with a diverse student population. The purpose of this paper is to illustrate if the implementation of Technology is the solution to the Problems faced in Higher Education. The paper builds upon a constructivist approach, combining a literature review and research on key publications and academic reports.

Keywords: artificial intelligence, learning platforms, students personalised needs, life- long learning, privacy, ethics

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Authors: Enya Autumn Trenholm-Jensen, Hjalte Hviid Mikkelsen

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This study was done in the pursuit of nuancing the discussion surrounding what it means to be human in a time of unparalleled technological development. Subjectivity was deemed to be an accessible example of humanity to study, and art was a fitting medium through which to probe subjectivity. Upon careful theoretical consideration, abstract art was found to fit the parameters of the study with the added bonus of being, as of yet, uninterpretable from an AI perspective. It was hypothesised that dissimilar appraisals of the art stimuli would be found through sentiment and terminology. Opinion data was collected through survey responses and analysed using Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analysis. The results reflected the enigmatic nature of subjectivity through erratic ratings of the art stimuli. However, significant themes were found in the terminology used in the responses. The implications of the findings are discussed in relation to the uniqueness, or lack thereof, of human subjectivity, and directions for future research are provided.

Keywords: abstract art, artificial intelligence, cognition, sentiment, subjectivity

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Authors: Quentin C.P. Bourgogne, Vanessa Bouchart, Pierre Chevrier, Emmanuel Dattoli

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This study presents an experimental and theoretical work conducted on a PolyPhenylene Sulfide reinforced with 40%wt of short glass fibers (PPS GF40) and its matrix. Thermoplastics are widely used in the automotive industry to lightweight automotive parts. The replacement of metallic parts by thermoplastics is reaching under-the-hood parts, near the engine. In this area, the parts are subjected to high temperatures and are immersed in cooling liquid. This liquid is composed of water and glycol and can affect the mechanical properties of the composite. The aim of this work was thus to quantify the evolution of mechanical properties of the thermoplastic composite, as a function of temperature and liquid aging effects, in order to develop a reliable design of parts. An experimental campaign in the tensile mode was carried out at different temperatures and for various glycol proportions in the cooling liquid, for monotonic and cyclic loadings on a neat and a reinforced PPS. The results of these tests allowed to highlight some of the main physical phenomena occurring during these solicitations under tough hydro-thermal conditions. Indeed, the performed tests showed that temperature and liquid cooling aging can affect the mechanical behavior of the material in several ways. The more the cooling liquid contains water, the more the mechanical behavior is affected. It was observed that PPS showed a higher sensitivity to absorption than to chemical aggressiveness of the cooling liquid, explaining this dominant sensitivity. Two kinds of behaviors were noted: an elasto-plastic type under the glass transition temperature and a visco-pseudo-plastic one above it. It was also shown that viscosity is the leading phenomenon above the glass transition temperature for the PPS and could also be important under this temperature, mostly under cyclic conditions and when the stress rate is low. Finally, it was observed that soliciting this composite at high temperatures is decreasing the advantages of the presence of fibers. A new phenomenological model was then built to take into account these experimental observations. This new model allowed the prediction of the evolution of mechanical properties as a function of the loading environment, with a reduced number of parameters compared to precedent studies. It was also shown that the presented approach enables the description and the prediction of the mechanical response with very good accuracy (2% of average error at worst), over a wide range of hydrothermal conditions. A temperature-humidity equivalence principle was underlined for the PPS, allowing the consideration of aging effects within the proposed model. Then, a limit of improvement of the reachable accuracy was determinate for all models using this set of data by the application of an artificial intelligence-based model allowing a comparison between artificial intelligence-based models and phenomenological based ones.

Keywords: aging, analytical modeling, mechanical testing, polymer matrix composites, sequential model, thermomechanical

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Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

Abstract:

Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu

Abstract:

Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

Abstract:

In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

Abstract:

An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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Authors: Sani Mohammed Adam

Abstract:

This work seeks to review the security challenges facing Nigeria and explore the relevance of laws and policies in tackling the menace. The work looks at the adequacy of available legislations and the functionality of relevant institutions such as the Armed Forces, the Nigeria Police Force, the State Security Service, the Defence Intelligence Agency and the Nigerian Intelligence Agency etc. Comparisons would be made with other jurisdictions, such as inter alia, the Homeland Security in the USA and Counter Terrorism Laws of the United Kingdom. Recommendations would be made on how to strengthen both institutions and laws to curtail the growth of Terrorism in Nigeria.

Keywords: legislations, Nigeria, security, terrorism

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Authors: Colin Schmidt, Adrien Lecossier, Pascal Crubleau, Philippe Blanchard, Simon Richir

Abstract:

Introduction:The widespread availability of artificial intelligence, exemplified by Generative Pre-trained Transformers (GPT) relying on large language models (LLM), has caused a seismic shift in the realm of knowledge. Everyone now has the capacity to swiftly learn how these models can either serve them well or not. Today, conversational AI like ChatGPT is grounded in neural transformer models, a significant advance in natural language processing facilitated by the emergence of renowned LLMs constructed using neural transformer architecture. Inventiveness of an LLM : OpenAI's GPT-3 stands as a premier LLM, capable of handling a broad spectrum of natural language processing tasks without requiring fine-tuning, reliably producing text that reads as if authored by humans. However, even with an understanding of how LLMs respond to questions asked, there may be lurking behind OpenAI’s seemingly endless responses an inventive model yet to be uncovered. There may be some unforeseen reasoning emerging from the interconnection of neural networks here. Just as a Soviet researcher in the 1940s questioned the existence of Common factors in inventions, enabling an Under standing of how and according to what principles humans create them, it is equally legitimate today to explore whether solutions provided by LLMs to complex problems also share common denominators. Theory of Inventive Problem Solving (TRIZ) : We will revisit some fundamentals of TRIZ and how Genrich ALTSHULLER was inspired by the idea that inventions and innovations are essential means to solve societal problems. It's crucial to note that traditional problem-solving methods often fall short in discovering innovative solutions. The design team is frequently hampered by psychological barriers stemming from confinement within a highly specialized knowledge domain that is difficult to question. We presume ChatGPT Utilizes TRIZ 40. Hence, the objective of this research is to decipher the inventive model of LLMs, particularly that of ChatGPT, through a comparative study. This will enhance the efficiency of sustainable innovation processes and shed light on how the construction of a solution to a complex problem was devised. Description of the Experimental Protocol : To confirm or reject our main hypothesis that is to determine whether ChatGPT uses TRIZ, we will follow a stringent protocol that we will detail, drawing on insights from a panel of two TRIZ experts. Conclusion and Future Directions : In this endeavor, we sought to comprehend how an LLM like GPT addresses complex challenges. Our goal was to analyze the inventive model of responses provided by an LLM, specifically ChatGPT, by comparing it to an existing standard model: TRIZ 40. Of course, problem solving is our main focus in our endeavours.

Keywords: artificial intelligence, Triz, ChatGPT, inventiveness, problem-solving

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