Search results for: Emotional intelligence

Authors: Fanny Soulard

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Today work environment is more multicultural than ever: spatial limits have been blown out, encouraging people and ideas mobility all around the globe. Indeed, opportunities to design with culturally diverse team workers, clients, or end-users, have become within everyone's reach. We enjoy traveling to discover other civilizations, but when it comes to business, we often take for granted that our own work methodology will be generic enough to federate each party and cover the project needs. This paper aims to explore why, by skipping cultural awareness, we often create misunderstandings, frustration, and even counterproductive design. Tree lighting projects successively developed by a French lighting studio, a Vietnamese lighting studio, and an Australian Engineering company will be assessed from their concept stage to completion. All these study cases are based in Vietnam, where the construction market is equally led by local and international consultants. Core criteria such as lighting standard reference, service scope, communication tools, internal team organization, delivery package content, key priorities, and client relationship will help to spot and list when and how cultural diversity has impacted the design output and effectiveness. On the second hand, we will demonstrate through the same selected projects how intercultural intelligence tools and mindset can not only respond positively to previous situations and avoid major clashes but also turn cultural differences into a key added value to generate significant benefits for individuals, teams, and companies. By understanding the major importance of including a cultural factor within any design, intercultural intelligence will quickly turn out as a “must have” skill to be developed and acquired by any designer.

Keywords: intercultural intelligence, lighting design, work methodology, multicultural diversity

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Authors: Damien Mouratille, Antonio R. Hidalgo-Muñoz, Nadine Matton, Yves Rouillard, Mickael Causse, Radouane El Yagoubi

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Background: The monitoring of the physiological activity related to mental workload (MW) on pilots will be useful to improve aviation safety by anticipating human performance degradation. The electrocardiogram (ECG) can reveal MW fluctuations due to either cognitive workload or/and emotional state since this measure exhibits autonomic nervous system modulations. Arguably, heart rate (HR) is one of its most intuitive and reliable parameters. It would be particularly interesting to analyze the interaction between cognitive requirements and emotion in ecologic sets such as a flight simulator. This study aims to explore by means of HR the relation between cognitive demands and emotional activation. Presumably, the effects of cognition and emotion overloads are not necessarily cumulative. Methodology: Eight healthy volunteers in possession of the Private Pilot License were recruited (male; 20.8±3.2 years). ECG signal was recorded along the whole experiment by placing two electrodes on the clavicle and left pectoral of the participants. The HR was computed within 4 minutes segments. NASA-TLX and Big Five inventories were used to assess subjective workload and to consider the influence of individual personality differences. The experiment consisted in completing two dual-tasks of approximately 30 minutes of duration into a flight simulator AL50. Each dual-task required the simultaneous accomplishment of both a pre-established flight plan and an additional task based on target stimulus discrimination inserted between Air Traffic Control instructions. This secondary task allowed us to vary the cognitive workload from low (LC) to high (HC) levels, by combining auditory and visual numerical stimuli to respond to meeting specific criteria. Regarding emotional condition, the two dual-tasks were designed to assure analogous difficulty in terms of solicited cognitive demands. The former was realized by the pilot alone, i.e. Low Arousal (LA) condition. In contrast, the latter generates a high arousal (HA), since the pilot was supervised by two evaluators, filmed and involved into a mock competition with the rest of the participants. Results: Performance for the secondary task showed significant faster reaction times (RT) for HA compared to LA condition (p=.003). Moreover, faster RT was found for LC compared to HC (p < .001) condition. No interaction was found. Concerning HR measure, despite the lack of main effects an interaction between emotion and cognition is evidenced (p=.028). Post hoc analysis showed smaller HR for HA compared to LA condition only for LC (p=.049). Conclusion. The control of an aircraft is a very complex task including strong cognitive demands and depends on the emotional state of pilots. According to the behavioral data, the experimental set has permitted to generate satisfactorily different emotional and cognitive levels. As suggested by the interaction found in HR measure, these two factors do not seem to have a cumulative impact on the sympathetic nervous system. Apparently, low cognitive workload makes pilots more sensitive to emotional variations. These results hint the independency between data processing and emotional regulation. Further physiological data are necessary to confirm and disentangle this relation. This procedure may be useful for monitoring objectively pilot’s mental workload.

Keywords: cognitive demands, emotion, flight simulator, heart rate, mental workload

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: James Boag

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The needs of people with a diagnosis of dementia and their carers and families are physical, psychosocial, and psychological and begin at the time of diagnosis. There is frequently a lack of emotional support and counselling. Care- giving support is required from the presentation of the first symptoms of dementia until death. Alzheimer's disease begins decades before the clinical symptoms begin to appear, and in many cases, it remains undiagnosed, or diagnosed too late for any possible interventions to have any effect. However, if an incorrect diagnosis is given, it may result in a person being treated, without effect, for a type of dementia they do not have and delaying the interventions they should have received. Being diagnosed with dementia can cause emotional distress to the person, and physical and emotional support is needed, which will become more important as the disease progresses. The severity of the patient's dementia and their symptoms has a bearing of the impact on the carer and the support needed. A lack of insight and /or a denial of the diagnosis, grief, reacting to anticipated future losses, and coping methods to maximise the disease outcome, are things that should be addressed. Because of the stigma, it is important for carers not to lose contact with family and others because social isolation leads to depression and burnout. The impact on a carer's well- being and quality of life can be influenced by the severity of the illness, its type of dementia, its symptoms, healthcare support, financial and social status, career, age, health, residential setting, and relationship to the patient. Carer burnout due to lack of support leads to people diagnosed with dementia being put into residential care prematurely. Often dementia is not recognised as a terminal illness, limiting the ability of the person diagnosed with dementia and their carers to work on advance care planning and getting access to palliative and other support. Many carers have been satisfied with the physical support they were given in their everyday life, however, it was agreed that there was an immense unmet need for psychosocial support, especially after diagnosis and approaching end of life. Providing continuity and coordination of care is important. Training is necessary for providers to understand that every case is different, and they should understand the complexities. Grief, the emotional response to loss, is suffered during the progression of the disease and long afterwards, and carers should continue to be supported after the death of the person they were caring for.

Keywords: dementia, caring, challenges, needs

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Authors: Urva Maryam

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Information has become an important asset to the current cosmos. Globally, various tactics are being observed to confine the spread of information as it makes people vulnerable to security attacks. Open Source Intelligence (OSINT) is a publicly available source that has disseminated information about users or website, companies, and various organizations. This paper focuses on the quantitative method of exploring various OSINT tools that reveal public information of personals. This information could further facilitate the phishing attacks. Phishing attacks can be launched on email addresses, open ports, and unsecured web-surfing. This study allows to analyze information retrieved from OSINT tools i.e., the Harvester, and Maltego, that can be used to send phishing attacks to individuals.

Keywords: OSINT, phishing, spear phishing, email spoofing, the harvester, maltego

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Authors: Najma Najam, Rukhsana Kausar, Rabia Hussain Kanwal, Saira Batool, Anum Javed

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In Pakistan, elderly population though increasing but it has been neglected by the researchers and policy makers which resulted in compromised quality of life of the ageing population. Two regions, Punjab and Gilgit-Baltistan (GB) were selected for comparison as Lahore and Multan (Punjab) are highly urbanized, large cities whereas Gilgit and Skardu are remote and mountain bounded valleys in GB. This study focuses on psychological and emotional functioning of elderly and a series of measures translated and adapted in Urdu language was used to assess quality of life, psychological and mental well-being, actual and perceived social support, attachment patterns, forgiveness, affects, geriatric depression, and emotional disturbance patterns (depression, anxiety, and stress) in elderly. A gender-equated sample of 201 elderly participants, 93 from GB (60 from Gilgit, 33 from Skardu) and 108 from Punjab (61 from Lahore, 47 from Multan) with over 60 years age was collected from the multiethnic community of Punjab and GB through purposive convenient sampling technique. Findings revealed that elderly from Multan have better psychological and emotional functioning, higher levels of social support, tendency to forgive, better mental wellbeing and quality of life and lower levels of stress, anxiety, depression, negative affect and attachment avoidance and anxiety related to partner as compared to the elderly from Lahore. Furthermore, both elderly male of Gilgit & Skardu have adequate mental well-being including subjective well-being and psychological functioning which showed positive aspects of mental health but elderly female are more attached to their home and neighbourhood which shows their social and environmental mastery. Gilgiti elderly male reported more degree of positive affect such as enthusiasm, active, alertness, excitement and strong whereas among elderly from Skardu shows more negative affect i.e. aversive mood states, irritability, hostility, and general distress. The need of psychosocial therapy and family counseling for the elderly in urban areas has been identified, which can facilitate in reducing or preventing the depressive and stressful tendencies. The findings are expected to have implications for improving quality of life of the elderly, designing interventions, support system and rehabilitation services to help them. However, findings may attract attention of policy makers and researchers as currently this is the most neglected population in Pakistan.

Keywords: psychological, emotional, aging, elderly, quality of life

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Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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Authors: Yan Jia China

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The ruins is a kind of historical intention, which is also the current real existence of developing city. Zen culture of ancient China has a profound esthetic emotion, similarly, the west establish the concept of aesthetics of relic along with the Romanism’s (such as Rousseau etc.) sentiment to historical ruins at the end of 18th century. Nowadays, with the decline of traditional industrial society as well as the rise of post-industrial age, contemporary society must face the ruins and garbage problem which is left by industrial society. Commencing from the perspective of emotion and memory, this paper analyzes the importance for emotional needs as well as their existing status of several projects, such as the Capital Steelworks in Beijing (industrial devastation), the Shibati old section in Chongqing (urban slums) and the Old Hurva Synagogue in Jerusalem (ruins of war). It emphasizes urban design which is started from emotion and the sustainable development of city memory through managing the urban ruins which is criticized by people with the perspective of ecology and art.

Keywords: cultural heritage, urban ruins, ecology, emotion, sustainable urban memory

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Elisa Santana-Monagas, Juan L. Núñez, Jaime León, Samuel Falcón, Celia Fernández, Rocío P. Solís

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Teaching practices have been widely studied in relation to students' outcomes, positioning themselves as one of their strongest catalysts and influencing students' emotional experiences. In the higher education context, teachers become even more crucial as many students ground their decisions on which courses to enroll in based on opinions and ratings of teachers from other students. Unfortunately, sometimes universities do not provide the personal, social, and academic stimulation students demand to be actively engaged. To evaluate their teachers, universities often rely on students' evaluations of teaching (SET) collected via Likert scale surveys. Despite its usefulness, such a method has been questioned in terms of validity and reliability. Alternatively, researchers can rely on qualitative answers to open-ended questions. However, the unstructured nature of the answers and a large amount of information obtained requires an overwhelming amount of work. The present work presents an alternative approach to analyse such data: sentiment analysis. To the best of our knowledge, no research before has included results from SA into an explanatory model to test how students' sentiments affect their emotional engagement in class. The sample of the present study included a total of 225 university students (Mean age = 26.16, SD = 7.4, 78.7 % women) from the Educational Sciences faculty of a public university in Spain. Data collection took place during the academic year 2021-2022. Students accessed an online questionnaire using a QR code. They were asked to answer the following open-ended question: "If you had to explain to a peer who doesn't know your teacher how he or she communicates in class, what would you tell them?". Sentiment analysis was performed using Microsoft's pre-trained model. The reliability of the measure was estimated between the tool and one of the researchers who coded all answers independently. The Cohen's kappa and the average pairwise percent agreement were estimated with ReCal2. Cohen's kappa was .68, and the agreement reached was 90.8%, both considered satisfactory. To test the hypothesis relations among SA and students' emotional engagement, a structural equation model (SEM) was estimated. Results demonstrated a good fit of the data: RMSEA = .04, SRMR = .03, TLI = .99, CFI = .99. Specifically, the results showed that student’s sentiment regarding their teachers’ teaching positively predicted their emotional engagement (β == .16 [.02, -.30]). In other words, when students' opinion toward their instructors' teaching practices is positive, it is more likely for students to engage emotionally in the subject. Altogether, the results show a promising future for sentiment analysis techniques in the field of education. They suggest the usefulness of this tool when evaluating relations among teaching practices and student outcomes.

Keywords: sentiment analysis, students' evaluation of teaching, structural-equation modelling, emotional engagement

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Authors: Michaela Porubanova

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In this exploratory study to examine the effects of emotional significance on change detection using the flicker paradigm, three different categories of scenes were randomly presented (neutral, positive and negative) in three different blocks. We hypothesized that because of the different effects on attention, performance in change detection tasks differs for scenes with different effective values. We found the greatest accuracy of change detection was for changes occurring in positive and negative scenes (compared with neutral scenes). Secondly and most importantly, changes in negative scenes (and also positive scenes, though not with statistical significance) were detected faster than changes in neutral scenes. Interestingly, women were less accurate than men in detecting changes in emotionally significant scenes (both negative and positive), i.e., women detected fewer changes in emotional scenes in the time limit of 40s. But on the other hand, women were quicker to detect changes in positive and negative images than men. The study makes important contributions to the area of the role of emotions on information processing. The role of emotion in attention will be discussed.

Keywords: attention, emotion, flicker task, IAPS

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Authors: Hannah Bigelow, Marcus D. Gottlieb, Michelle Ogrodnik, Jeffrey, D. Graham, Barbara Fenesi

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Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders affecting approximately 6% of children worldwide. ADHD is characterized by a combination of persistent deficits including impaired inhibitory control, working memory and task-switching. Many children with ADHD also have comorbid mental health issues such as anxiety and depression. There are several treatment options to manage ADHD impairments, including drug and behavioural management therapy, but they all have drawbacks, such as worsening mood disturbances or being inaccessible to certain demographics. Both physical exercise and mindfulness meditation serve as alternative options to potentially help mitigate ADHD symptoms. Although there is extensive support for the benefits of long-term physical exercise or mindfulness meditation programs, there is insufficient research investigating how acute bouts (i.e., single, short bouts) can help children with ADHD. Thus, the current study aimed to understand how single, short bouts of exercise and mindfulness meditation impacts executive functioning and psycho-emotional well-being in children with ADHD, as well as to directly compare the efficacy of these two interventions. The study used a a pre- post-test, within-subjects design to assess the effects of a 10-minute bout of moderate intensity exercise versus a 10-minute bout of mindfulness meditation (versus 10 minutes of a reading control) on the executive functioning and psycho-emotional well-being of 16 children and youth with ADHD aged 10-14 (male=11; White=80%). Participants completed all three interventions: 10 minutes of exercise, 10 minutes of mindfulness meditation, and 10 minutes of reading (control). Executive functioning (inhibitory control, working memory, task-switching) and psycho-emotional well-being (mood, self-efficacy) were assessed before and after each intervention. Mindfulness meditation promoted executive functioning, while exercise enhanced positive mood and self-efficacy. Critically, this work demonstrates that a single, short bout of mindfulness meditation session can promote inhibitory control among children with ADHD. This is especially important for children with ADHD as inhibitory control deficits are among the most pervasive challenges that they face. Furthermore, the current study provides preliminary evidence for the benefit of acute exercise for promoting positive mood and general self-efficacy for children and youth with ADHD. These results may increase the accessibility of acute exercise for children with ADHD, providing guardians and teachers a feasible option to incorporate just 10 minutes of exercise to assist children emotionally. In summary, this research supports the use of acute exercise and mindfulness meditation on varying aspects of executive functioning and psycho-emotional well-being in children and youth with ADHD. This work offers important insight into how behavioural interventions could be personalized according to a child’s needs.

Keywords: attention-deficit hyperactivity disorder (ADHD), acute exercise, mindfulness meditation, executive functioning, psycho-emotional well-being

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Fausto Morais

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Artificial intelligence has been widely used in law. Programs are able to classify suits, to identify decision-making patterns, to predict outcomes, and to formalize legal arguments as well. In Brazil, the artificial intelligence victor has been classifying cases to supreme court’s standards. When those programs act doing those tasks, they simulate some kind of legal decision and legal arguments, raising doubts about how artificial intelligence can be integrated into legal reasoning. Taking this into account, the following three issues are identified; the problem of hypernormatization, the argument of legal anthropocentrism, and the artificial legal principles. Hypernormatization can be seen in the Brazilian legal context in the Supreme Court’s usage of the Victor program. This program generated efficiency and consistency. On the other hand, there is a feasible risk of over standardizing factual and normative legal features. Then legal clerks and programmers should work together to develop an adequate way to model legal language into computational code. If this is possible, intelligent programs may enact legal decisions in easy cases automatically cases, and, in this picture, the legal anthropocentrism argument takes place. Such an argument argues that just humans beings should enact legal decisions. This is so because human beings have a conscience, free will, and self unity. In spite of that, it is possible to argue against the anthropocentrism argument and to show how intelligent programs may work overcoming human beings' problems like misleading cognition, emotions, and lack of memory. In this way, intelligent machines could be able to pass legal decisions automatically by classification, as Victor in Brazil does, because they are binding by legal patterns and should not deviate from them. Notwithstanding, artificial intelligent programs can be helpful beyond easy cases. In hard cases, they are able to identify legal standards and legal arguments by using machine learning. For that, a dataset of legal decisions regarding a particular matter must be available, which is a reality in Brazilian Judiciary. Doing such procedure, artificial intelligent programs can support a human decision in hard cases, providing legal standards and arguments based on empirical evidence. Those legal features claim an argumentative weight in legal reasoning and should serve as references for judges when they must decide to maintain or overcome a legal standard.

Keywords: artificial intelligence, artificial legal principles, hypernormatization, legal anthropocentrism argument, legal reasoning

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Authors: Kwame Amoah

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Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: M. E. Joret, F. Germeys, P. Van de Craen

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Background: Previous studies have shown that ‘full’ bilingualism seems to enhance the executive functions in children, young adults and elderly people. Executive functions refer to a complex cognitive system responsible for self-controlled and planned behavior and seem to predict academic achievement. The present study aimed at investigating whether similar effects could be found in children learning their second language at school in immersion education programs. Methods: In this study, 44 children involved in immersion education for 4 to 5 years were compared to 48 children in traditional schools. All children were between 9 and 11 years old. To assess executive functions, the Simon task was used, a neuropsychological measure assessing executive functions with reaction times and accuracy on congruent and incongruent trials. To control for background measures, all children underwent the Raven’s coloured progressive matrices, to measure non-verbal intelligence and the Echelle de Vocabulaire en Images Peabody (EVIP), assessing verbal intelligence. In addition, a questionnaire was given to the parents to control for other confounding variables, such as socio-economic status (SES), home language, developmental disorders, etc. Results: There were no differences between groups concerning non-verbal intelligence and verbal intelligence. Furthermore, the immersion learners showed overall faster reaction times on both congruent and incongruent trials compared to the traditional learners, but only after 5 years of training, not before. Conclusion: These results show that the cognitive benefits found in ‘full’ bilinguals also appear in children involved in immersion education, but only after a sufficient exposure to the second language. Our results suggest that the amount of second language training needs to be sufficient before these cognitive effects may emerge.

Keywords: bilingualism, executive functions, immersion education, Simon task

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Authors: Phillip Garrad, Saritha Unnikrishnan

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The recent popularity of connected and autonomous vehicles (CAV) corresponds with an increase in the risk of cyber-attacks. These cyber-attacks have been instigated by both researchers or white-coat hackers and cyber-criminals. As Connected Vehicles move towards full autonomy, the impact of these cyber-attacks also grows. The current research details challenges faced in cybersecurity testing of CAV, including access and cost of the representative test setup. Other challenges faced are lack of experts in the field. Possible solutions to how these challenges can be overcome are reviewed and discussed. From these findings, a software simulated CAV network is established as a cost-effective representative testbed. Penetration tests are then performed on this simulation, demonstrating a cyber-attack in CAV. Studies have shown Artificial Intelligence (AI) to improve runtime, increase efficiency and comprehensively cover all the typical test aspects in penetration testing in other industries. There is an attempt to introduce similar AI models to the software simulation. The expectation from this implementation is to see similar improvements in runtime and efficiency for the CAV model. If proven to be an effective means of penetration test for CAV, this methodology may be used on a full CAV test network.

Keywords: cybersecurity, connected vehicles, software simulation, artificial intelligence, penetration testing

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Authors: Usha Barahmand, Ali Khazaee, Goudarz Sadeghi Hashjin

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The purpose of this study is to assess the relative mediating effects of impulsivity and internalizing problems in the relationship between childhood neglect and motives for opiate use. Seventy-two adolescent opiate users were recruited for the study. Participants completed assessments of childhood abuse history, distress, impulsiveness and motives for substance use as well as a socio-demographic information sheet. Findings from bootstrap mediator analyses indicated that distress, but not impulsiveness, mediated the relationship between childhood emotional abuse and expansion and enhancement motives for substance use. The current study provides preliminary evidence that internalizing problems may function as a mechanism linking prior childhood experiences of emotional neglect to subsequent motives for substance use. Clinical implications of these findings suggest that targeting emotion dysregulation problems may be an effective adjunct in the treatment of adolescents with a history of childhood maltreatment that are at risk for substance use.

Keywords: childhood neglect, impulsiveness, internalizing problems, substance use motives

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Authors: Seyed Esmail Seyedi Bariran, Laysheng Ewe, Amy Ling

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Since supplier selection appears as a vital decision, selecting supplier based on the best and most accurate ways has a lot of importance for enterprises. In this study, a new Artificial Intelligence approach is exerted to remove weaknesses of supplier selection. The paper has three parts. First part is choosing the appropriate criteria for assessing the suppliers’ performance. Next one is collecting the data set based on experts. Afterwards, the data set is divided into two parts, the training data set and the testing data set. By the training data set the best structure of GEP and ANN are selected and to evaluate the power of the mentioned methods the testing data set is used. The result obtained shows that the accuracy of GEP is more than ANN. Moreover, unlike ANN, a mathematical equation is presented by GEP for the supplier selection.

Keywords: supplier selection, automotive supply chains, ANN, GEP

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Authors: Jarbas Lopes Cardoso Jr., Frederic Andres, Alexandre Guitton, Asanee Kawtrakul, Silvio E. Barbin

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The important objective of the CyberBrain Mass Agriculture Alarm Acquisition and Analysis (CBMa4) project is to minimize the impacts of diseases and disasters on rice cultivation. For example, early detection of insects will reduce the volume of insecticides that is applied to the rice fields through the use of CBMa4 platform. In order to reach this goal, two major factors need to be considered: (1) the social network of smart farmers; and (2) the warning data alarm acquisition and analysis component. This paper outlines the process for collecting the warning and improving the decision-making result to the warning. It involves two sub-processes: the warning collection and the understanding enrichment. Human sensors combine basic suitable data processing techniques in order to extract warning related semantic according to collective intelligence. We identify each warning by a semantic content called 'warncons' with multimedia metaphors and metadata related to these metaphors. It is important to describe the metric to measuring the relation among warncons. With this knowledge, a collective intelligence-based decision-making approach determines the action(s) to be launched regarding one or a set of warncons.

Keywords: agricultural engineering, warning systems, social network services, context awareness

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Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: William Marquis, Fang Dong

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There has been a growing concern in recent years over the economic and social effects of adult video gaming. It has been estimated that the number of people who played video games during the COVID-19 pandemic is close to three billion, and there is evidence that this form of entertainment is here to stay. Many people are concerned that this growing use of time could crowd out time that could be spent on alternative forms of entertainment with family, friends, sports, and other social activities that build community. For example, recent studies of children suggest that playing videogames crowds out time that could be spent on homework, watching TV, or in other social activities. Similar studies of adults have shown that video gaming is negatively associated with earnings, time spent at work, and socializing with others. The primary objective of this paper is to examine how time adults spend on video gaming could displace time they could spend working and on activities that enhance their health and well-being. We use data from the American Time Use Survey (ATUS), maintained by the Bureau of Labor Statistics, to analyze the effects of time-use decisions on three measures of well-being. We pool the ATUS Well-being Module for multiple years, 2010, 2012, 2013, and 2021, along with the ATUS Activity and Who files for these years. This pooled data set provides three broad measures of well-being, e.g., health, life satisfaction, and emotional well-being. Seven variants of each are used as a dependent variable in different multivariate regressions. We add to the existing literature in the following ways. First, we investigate whether the time adults spend in video gaming crowds out time spent working or in social activities that promote health and life satisfaction. Second, we investigate the relationship between adult gaming and their emotional well-being, also known as negative or positive affect, a factor that is related to depression, health, and labor market productivity. The results of this study suggest that the time adult gamers spend on video gaming has no effect on their supply of labor, a negligible effect on their time spent socializing and studying, and mixed effects on their emotional well-being, such as increasing feelings of pain and reducing feelings of happiness and stress.

Keywords: online gaming, health, social capital, emotional wellbeing

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Authors: Thiago Fabrizio Gavilano Farje, Marcelo Patricio Herrera Malpartida

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This study examines the influence of artificial intelligence (AI) on the work efficiency of administrative employees in the financial sector of Metropolitan Lima, Peru, during the year 2024. Focusing on the relationship between AI implementation and work efficiency, it addresses specific variables such as decision-making, motivation, and employee productivity. To accomplish the analysis between AI and work efficiency within the financial sector of Metropolitan Lima, it is necessary to evaluate how AI optimizes time in administrative tasks, examine how AI impacts the agility of the process of making decisions, and investigate the influence of AI on the satisfaction and motivation of employees. The research adopts a correlational and explanatory approach, designed to establish and understand the connections between AI and work efficiency. A survey design adapted from an OECD study is used, applying questionnaires to a representative sample of administrative workers in the financial sector who incorporate AI into their functions. The target population includes administrative workers in the financial sector of Metropolitan Lima, estimated at 73,097 employees based on data from the Censo Nacional de Empresas y Establecimientos and studies by the BCRP. The sample, selected through simple random sampling, comprises 246 workers.

Keywords: business management, artificial intelligence, decision making, labor efficiency, financial sector

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Authors: Abigail Wiafe, Charles Nutrokpor, Adelaide Oduro-Asante

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The advancement of technology is rapidly making people more receptive to music as computer-generated music requires minimal human interventions. Though algorithms are applied to generate music, the human experience of emotions is still explored. Thus, this study investigates the emotions humans experience listening to computer-generated music that possesses aesthetic qualities. Forty-two subjects participated in the survey. The selection process was purely arbitrary since it was based on convenience. Subjects listened and evaluated the emotions experienced from the computer-generated music through an online questionnaire. The Likert scale was used to rate the emotional levels after the music listening experience. The findings suggest that computer-generated music possesses aesthetic qualities that do not affect subjects' emotions as long as they are pleased with the music. Furthermore, computer-generated music has unique creativity, and expressioneven though the music produced is meaningless, the computational models developed are unable to present emotional contents in music as humans do.

Keywords: aesthetic, algorithms, emotions, computer-generated music

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Authors: James V. Luisi

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Understanding consciousness requires a synthesis of ideas from multiple disciplines, including obvious ones like psychology, biology, evolution, neurology, and neuroscience, as well as less obvious ones like protozoology, botany, entomology, carcinology, herpetology, mammalogy, and computer sciences. Furthermore, to incorporate the necessary backdrop, it is best presented in a theme of Eastern philosophy, specifically leveraging the teachings of Buddhism for its relevance to early thought on consciousness. These ideas are presented as a multi-level framework that illustrates the various aspects of consciousness within a tapestry of foundational and dependent building blocks as to how living organisms evolved to understand elements of their reality sufficiently to survive, and in the case of Homo sapiens, eventually move beyond meeting the basic needs of survival, but to also achieve survival of the species beyond the eventual fate of our planet. This is not a complete system of thought, but just a framework of consciousness gathering some of the key elements regarding the evolution of consciousness and the advent of free will, and presenting them in a unique way that encourages readers to continue the dialog and thought process as an experience to enjoy long after reading the last page. Readers are encouraged to think for themselves about the issues raised herein and to question every facet presented, as much further exploration is needed. Needless to say, this subject will remain a rapidly evolving one for quite some time to come, and it is probably in the interests of everyone to at least consider attaining both an ability and willingness to participate in the dialog.

Keywords: consciousness, sentience, intelligence, artificial intelligence, Buddhism

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Authors: Sonia Arradaza-Pajaron

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This study examined the relationship between transformational leadership (TL) and self-efficacy (SE) of academic heads in the implementation of a customized English language curriculum (CELC) among technological state universities and colleges in Leyte provinces and Biliran, Philippines. Results manifested that academic leaders practiced transformational leadership and are self-efficacious enough but with only moderate level in the effectiveness of CELC implementation. It was found out; further, that of the four identified transformational leadership components, except idealized influence, three of which demonstrated a significant relationship with CELC component variables, although in varying degree. Moreover, self-efficacy sources, especially vicarious experiences and verbal persuasion manifested moderate to high significant relationships with effective CELC curriculum implementation. Further, verbal persuasion and physiological/emotional condition manifested significant relationship with CELC-resource and CELC-contextual/community influence, respectively. Regression analysis showed that TL-individualized consideration component explained wider extent when correlated with CELC contextual/community components, while self-efficacy source-verbal persuasion demonstrated a wider extent with the three CELC components, namely; resource, process and physiological/emotional condition. Results further revealed that TL-individualized consideration manifested lesser influence with CELC implementation, while SE-verbal persuasion demonstrated stronger influence or effect on CELC-process, CELC-physiological/emotional, while lesser influence with CELC-resource. This implies that academic leaders, in order to carry out effective curriculum implementation, should provide more emphasis on school culture, its beliefs, practices and academic atmosphere but most of all empower human resources who are considered the backbone of the work place and can be directly affected by any curriculum shifts and challenges. To realize this, more values-skilled training programs must be designed for academic heads are needed to equip them with the necessary leadership skills, beliefs in their capacity to lead and their own enhance emotional well-being in leading subordinates and facilitating curriculum implementation.

Keywords: Customized English Language curriculum, CELC, self-efficacy, transformational leadership, values-skilled training

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