Search results for: vulnerability intelligence

Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: J. J. Lloyd, J. S. Phillips

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Background: HIV/AIDS has made a huge impact on human development and sexual reproductive habits in this century in the world and especially in sub-Saharan Africa. It has only recently been acknowledged that HIV/AIDS has an equal if not greater effect on or threat to people with disabilities. Survivors of traumatic spinal cord injury (SCI) with resultant disability are incorrectly believed to be sexually inactive, unlikely to use drugs or alcohol and at less risk of violence or rape than their non-disabled peers. This group can thus be described as economically, educationally and socially disadvantaged, which in itself, suggest that they are a high-risk group for HIV infection. Objectives: Thus, the overall objective of this study was to assess the factors that exacerbate the risk and vulnerability of individuals with spinal cord injuries to HIV infection in order to develop a more effective HIV intervention. Methodology: This paper reports on the cross-sectional data gathered from individuals with a traumatic spinal cord injury in 4 conveniently selected provinces in South Africa. Data was collected by means of self-administered questionnaires. The questionnaire consisted of various sections requesting for information on Demographics; HIV-Knowledge (HIV- KQ-18); Sexual behaviours; sexual communication, and negotiation skills and Self-efficacy to refuse sex. Results: The majority of the study sample was males (72.7%) with a mean age of 34.6 years. The majority reported lifetime sexual intercourse (92.4%) but only 31.8% reported condom use with last sexual intercourse. Low level of HIV knowledge, and being male were the strongest predictor of risky sexual behaviours in this sample. Conclusion: Significant numbers of individuals with spinal cord injuries are thus engaging in risky sexual behaviours pointing to a need to strengthen comprehensive sexual health education to increase access to HIV testing, promote safe sex and condom use among this group.

Keywords: Human Immunodeficiency Virus (HIV), individuals with spinal cord injuries, risky sexual behaviours, HIV risk factors, sub-saharan Africa

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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Authors: Chastiti Mediafira Wulolo, Tri Legionosuko, Suhirwan, Yusuf

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Terrorism and radicalization have become a common threat to every nation in this world. As a part of the asymmetric warfare threat, terrorism and radicalization need a complex strategy as the problem solver. One such way is by collaborating with the international community. The Our Eyes Initiative (OEI), for example, is a cooperation pact in the field of intelligence information exchanges related to terrorism and radicalization initiated by the Indonesian Ministry of Defence. The pact has been signed by Indonesia, Philippines, Malaysia, Brunei Darussalam, Thailand, and Singapore. This cooperation mostly engages military acts as a central role, but it still requires the involvement of various parties such as the police, intelligence agencies and other government institutions. This paper will use a qualitative content analysis method to address the opportunity and enhance the optimization of OEI. As the result, it will explain how OEI takes the opportunities as the strategy for counter-terrorism by building it up as the regional cooperation, building the legitimacy of government and creating the legal framework of the information sharing system.

Keywords: our eyes initiative, terrorism, counter-terrorism, ASEAN, cooperation, strategy

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences. Humanity has never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

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Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: Günther Schuh, Christina König, Toni Drescher

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One of the key factors for the competitiveness and market success of technology-driven companies is the timely provision of information about emerging technologies, changes in existing technologies, as well as relevant related changes in the market's structures and participants. Therefore, many companies conduct technology intelligence (TI) activities to ensure an early identification of appropriate technologies and other (weak) signals. One base activity of TI is technology monitoring, which is defined as the systematic tracking of developments within a specified topic of interest as well as related trends over a long period of time. Due to the very large number of dynamically changing parameters within the technological and the market environment of a company as well as their possible interdependencies, it is necessary to focus technology monitoring on specific indicators or other criteria, which are able to point out technological developments and market changes. In addition to the execution of a literature review on existing approaches, which mainly propose patent-based indicators, it is examined in this paper whether indicator systems from other branches such as risk management or economic research could be transferred to technology monitoring in order to enable an efficient and focused technology monitoring for companies.

Keywords: technology forecasting, technology indicator, technology intelligence, technology management, technology monitoring

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Authors: Shailendra M. Pradhan, Upendra M. Pradhan

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Disasters are perennial features of human civilization. The recurring earthquakes, floods, cyclones, among others, that result in massive loss of lives and devastation, is a grim reminder of the fact that, despite all our success stories of development, and progress in science and technology, human society is perennially at risk to disasters. The apparent threat of climate change and global warming only severe our disaster risks. Darjeeling hills, situated along Eastern Himalayan region of India, and famous for its three Ts – tea, tourism and toy-train – is also equally notorious for its disasters. The recurring landslides and earthquakes, the cyclone Aila, and the Ambootia landslides, considered as the largest landslide in Asia, are strong evidence of the vulnerability of Darjeeling hills to natural disasters. Given its geographical location along the Hindu-Kush Himalayas, the region is marked by rugged topography, geo-physically unstable structure, high-seismicity, and fragile landscape, making it prone to disasters of different kinds and magnitudes. Most of the studies on disasters in Darjeeling hills are, however, scientific and geographical in orientation that focuses on the underlying geological and physical processes to the neglect of social and political conditions. This has created a tendency among the researchers and policy-makers to endorse and promote a particular type of discourse that does not consider the social and political aspects of disasters in Darjeeling hills. Disaster, this paper argues, is a complex phenomenon, and a result of diverse factors, both physical and human. The hazards caused by the physical and geological agents, and the vulnerabilities produced and rooted in political, economic, social and cultural structures of a society, together result in disasters. In this sense, disasters are as much a result of political and economic conditions as it is of physical environment. The human aspect of disasters, therefore, compels us to address intricating social and political challenges that ultimately determine our resilience and vulnerability to disasters. Set within the above milieu, the aims of the paper are twofold: a) to provide a political and sociological account of disasters in Darjeeling hills; and, b) to identify and address the root causes of its vulnerabilities to disasters. In situating disasters in Darjeeling Hills, the paper adopts the Pressure and Release Model (PAR) that provides a theoretical insight into the study of social and political aspects of disasters, and to examine myriads of other related issues therein. The PAR model conceptualises risk as a complex combination of vulnerabilities, on the one hand, and hazards, on the other. Disasters, within the PAR framework, occur when hazards interact with vulnerabilities. The root causes of vulnerability, in turn, could be traced to social and political structures such as legal definitions of rights, gender relations, and other ideological structures and processes. In this way, the PAR model helps the present study to identify and unpack the root causes of vulnerabilities and disasters in Darjeeling hills that have largely remained neglected in dominant discourses, thereby providing a more nuanced and sociologically sensitive understanding of disasters.

Keywords: Darjeeling, disasters, PAR, vulnerabilities

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Authors: Prakash Singh

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Employees who are satisfied at their place of work rate their leaders’ intrapersonal competencies as being high. They also believe that a leader’s intrapersonal competencies influence their sense of job satisfaction. Employees who indicate that they are unhappy at their place of work rate their leaders’ intrapersonal competencies as being low. They also believe that a leader’s intrapersonal intelligence influence their feeling of job satisfaction. The leader’s appropriate intrapersonal competencies are crucial to the creation of a motivated and satisfied employee team. In this study, the quantitative research method was used to determine the employees’ perceptions of their leaders’ intrapersonal competencies and their influence on their job satisfaction; the six competencies being self-awareness, self-confidence, self-expression, self-control, adaptability, and optimism. All the competencies of leaders identified in this quantitative study can therefore be described as intervening variables that influence an employee’s sense of job satisfaction. The number of responses that indicate that each of the intrapersonal competencies of a leader that will have an influence on an employee’s sense of job satisfaction, ranges from 93% (a leader’s sense of self-awareness) to 99% (a leader’s ability to be adaptable). As the responses are significantly similar, it can be stated that the respondents indicate that all the intrapersonal competencies of a leader can influence an employee’s sense of job satisfaction. The findings of this study strongly suggest that in order to be satisfied at work, employees prefer to be led by leaders who are confident in their leadership roles; who send out clear, unambiguous messages; who maintain self-control; who are adaptable and flexible;, who face the future with optimism and who support the establishment of a collegial working environment. Evidently, the findings corroborate the hypothesis that employees believe that the intrapersonal competencies of leaders have a positive influence on the employees’ sense of job satisfaction. This study’s findings, therefore, confirm that the key to the leaders’ self-knowledge is access to their own feelings and the ability to discriminate among them and draw upon them to guide behaviour in their organisations. This exploratory study makes a contribution to the emerging research being accomplished on leaders’ intrapersonal intelligence with more research still needing to be attempted to determine to what extent these competencies of leaders can reshape the organizational climate and culture.

Keywords: emotional intelligence, employees’ job satisfaction, leaders’ intrapersonal competencies, leaders’ self-knowledge

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Laura Brennan, Ram Balakumar

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ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

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Authors: Sepinoud Hamedi

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Over the past few decades, companies have appeared developing concerns in connection to the natural affect of their fabricating exercises. Green supply chain administration has been considered by the producers as a attainable choice to decrease the natural affect of operations whereas at the same time moving forward their operational execution. Contemporaneously the coming of digitalization and globalization within the supply chain space has driven to a developing acknowledgment of the importance of data preparing methodologies, such as enormous information analytics and fake insights innovations, in improving and optimizing supply chain execution. Also, supply chain collaboration in part intervenes the relationship between manufactured innovation and supply chain execution Ponders appear that the use of BDA-AI advances includes a significant impact on natural handle integration and green supply chain collaboration conjointly underlines that both natural handle integration and green supply chain collaboration have a critical affect on natural execution. Correspondingly savvy supply chain contributes to green execution through overseeing green connections and setting up green operations.

Keywords: green supply chain, artificial intelligence, manufacturers, technology, environmental

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Authors: Muneeb Ahmad, Ali Raza

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A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR.

Keywords: artificial innate immune system, flocking swarm, immune system, swarm intelligence

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Authors: Divine Agozie, Muesser Nat, Eric Afful-Dadzie

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This examination attempts an analysis of the effect of business intelligence and analytics (BI&A) capabilities on organizational role stressors and the implications of such an effect on performance. Two hundred twenty-eight responses gathered from seventy-six firms across Ghana were analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to validate the hypothesized relationships identified in the research model. Findings suggest both endogenous and exogenous dependencies of the sensing capability on the multiple role requirements of personnel. Further, transforming capability increases role conflict, whereas driving capability of BI&A systems impacts role conflict and role ambiguity. This study poses many practical insights to firms seeking to acquire analytics capabilities to drive performance and data-driven decision-making. It is important for firms to consider balancing role changes and task requirements before implementing and post-implementation stages of BI&A innovations.

Keywords: business intelligence and analytics, dynamic capabilities view, organizational stressors, structural equation modelling

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Authors: Lejla Sunagic

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The aim of the manuscript is to further the understanding of risky decision-making in the context of forced and irregular migration. The empirical evidence is collected through interviews with Syrian refugees who arrived in Europe via irregular pathways. Analytically, it has been approached through the juxtaposition between risk perception and the notion of desire. As different frameworks have been developed to address differences in risk perception, the common thread was the understanding that individual risk-taking has been addressed in terms of benefits outweighing risks. However, this framework cannot explain a big risk an individual takes because of an underprivileged position and due to a lack of positive alternatives, termed as risk-taking from vulnerability. The accounts of the field members of this study that crossed the sea in rubber boats to arrive in Europe make an empirical fit to such a postulate by reporting that the risk they have taken was not the choice but the only coping strategy. However, the vulnerability argument falls short of explaining why the interviewees, thinking retrospectively, find the risky journey they have taken to be worth it, while they would strongly advise others to restrain from taking such a huge risk. This inconsistency has been addressed by adding the notion of desire to migrate to the elements of risk perception. Desire, as a subjective experience, was what made the risk appear smaller in cost-benefit analysis at the time of decision-making of those who have realized migration. However, when they reflect on others in the context of potential migration via the same pathway, the interviewees addressed the others’ lack of capacity to avoid the same obstacles that they themselves were able to circumvent while omitting to reflect on others’ desire to migrate. Thus, in the risk-benefit analysis performed for others, the risk remains unblurred and tips over the benefits, given the inability to take into account the desire of others. If desire, as the transformative potential of migration, is taken out of the cost-benefit analysis of irregular migration, refugees might not have taken the risky journey. By casting the theoretical argument in the language of configuration, the study is filling in the gap of knowledge on the combination of migration drivers and the way they interact and produce migration outcomes.

Keywords: refugees, risk perception, desire, irregular migration

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Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

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

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Authors: Janaina Camile Pasqual Lofhagen, David Savarese, Veronika Vazhnik

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The urgency of the climate crisis requires both innovation and practicality. The energy transition framework allows industry to deliver resilient cities, enhance adaptability to change, pursue energy objectives such as growth or efficiencies, and increase renewable energy. This paper investigates a real-world application perspective for the use of biogas in Brazil and the U.S.. It will examine interventions to provide a foundation of infrastructure, as well as the tangible benefits for policy-makers crafting law and providing incentives.

Keywords: resilience, vulnerability, risks, biogas, sustainability.

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Authors: Tripureshwari Paul

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We are born with nature molded by nurture. Studies have confirmed the productive role of genes and environment on an individual. This study examines the relationship of parental genotype values on the intellectual ability of their children. Keeping in mind that academic achievement-learning capacity of student through normative education, a function of exposure to family environment and pathology with intellectual quotient of the individual. Purposive sampling was used and children between ages 11 and 12 years and their respective parents were involved. Raven’s Standard Progressive Matrices (RSPM), Family Pathology Scale (FPS) and Family Environment Scale (FES) were administered. The results found significant relationship of Offspring IQ to Parental IQ, maternal IQ demonstrating higher values of correlation. Female IQ was significant to maternal IQ and male IQ was significant to paternal IQ. With Academic Achievement not significantly correlated to IQ, it was determined that Competitive framework, freedom to expression and Recreational Orientation in family affect a child’s intellectual performance.

Keywords: academic achievement, environment, family environment, family pathology, genotype, intelligence quotient, maternal IQ, paternal IQ

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Authors: Lucilla Crosta, Anthony Edwards

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Companies and the market place nowadays struggle to find employees with adequate skills in relation to anticipated growth of their businesses. At least half of workers will need to undertake some form of up-skilling process in the next five years in order to remain aligned with the requests of the market . In order to meet these challenges, there is a clear need to explore the potential uses of AI (artificial Intelligence) based tools in assessing transversal skills (critical thinking, communication and soft skills of different types in general) of workers and adult students while empowering them to develop those same skills in a reliable trustworthy way. Companies seek workers with key transversal skills that can make a difference between workers now and in the future. However, critical thinking seems to be the one of the most imprtant skill, bringing unexplored ideas and company growth in business contexts. What employers have been reporting since years now, is that this skill is lacking in the majority of workers and adult students, and this is particularly visible trough their writing. This paper investigates how critical thinking and communication skills are currently developed in Higher Education environments through use of AI tools at postgraduate levels. It analyses the use of a branch of AI namely Machine Learning and Big Data and of Neural Network Analysis. It also examines the potential effect the acquisition of these skills through AI tools and what kind of effects this has on employability This paper will draw information from researchers and studies both at national (Italy & UK) and international level in Higher Education. The issues associated with the development and use of one specific AI tool Edulai, will be examined in details. Finally comparisons will be also made between these tools and the more recent phenomenon of Chat GPT and forthcomings and drawbacks will be analysed.

Keywords: critical thinking, artificial intelligence, higher education, soft skills, chat GPT

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Authors: Francesca Nicolosi, Teresa Cosola

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Climate changes in Mediterranean areas, such as the increase of average seasonal temperatures, the urban heat island phenomenon, the intensification of solar radiation and the extreme weather threats, cause disruption events, so that climate adaptation has become a pressing issue. Due to the strategic role that the built heritage holds in terms of environmental impact and energy waste and its potentiality, it is necessary to assess the vulnerability and the adaptive capacity of the existing building to climate change, in order to define different mitigation scenarios. The aim of this research work is to define an optimized and integrated methodology for the assessment of resilience levels and adaptation scenarios for existing buildings in Mediterranean urban areas. Moreover, the study of resilience indicators allows us to define building environmental and energy performance in order to identify the design and technological solutions for the improvement of the building and its urban area potentialities. The methodology identifies step-by-step different phases, starting from the detailed study of characteristic elements of urban system: climatic, natural, human, typological and functional components are analyzed in their critical factors and their potential. Through the individuation of the main perturbing factors and the vulnerability degree of the system to the risks linked to climate change, it is possible to define mitigation and adaptation scenarios. They can be different, according to the typological, functional and constructive features of the analyzed system, divided into categories of intervention, and characterized by different analysis levels (from the single building to the urban area). The use of software simulations allows obtaining information on the overall behavior of the building and the urban system, to generate predictive models in the medium and long-term environmental and energy retrofit and to make a comparative study of the mitigation scenarios identified. The studied methodology is validated on a case study.

Keywords: climate impact mitigation, energy efficiency, existing building heritage, resilience

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Authors: Alexander K. Edwards

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Introduction: The moral dimension of teacher education globally has assumed a new paradigm of thinking based on the public gain (return-on-investments), value-creation (quality), professionalism (practice), and business strategies (innovations). Abundant literature reveals an interesting revolutionary trend in complimenting the raising of teachers and academic performances. Because of the global competition in the knowledge-creation and service areas, the C21st teacher at all levels is expected to be resourceful, strategic thinker, socially intelligent, relationship aptitude, and entrepreneur astute. This study is a significant contribution to practice and innovations to raise exemplary or primal teachers. In this study, the qualities needed were considered as ‘Quadrant Intelligence (Q-i)’ model for a primal teacher leadership beyond the classroom. The researcher started by examining the issue of the majority of teachers in Ghana Education Services (GES) in need of this Q-i to be effective and efficient. The conceptual framing became determinants of such Q-i. This is significant for global employability and versatility in teacher education to create premium and primal teacher leadership, which are again gaining high attention in scholarship due to failing schools. The moral aspect of teachers failing learners is a highly important discussion. In GES, some schools score zero percent at the basic education certificate examination (BECE). The question is what will make any professional teacher highly productive, marketable, and an entrepreneur? What will give teachers the moral consciousness of doing the best to succeed? Method: This study set out to develop a model for primal teachers in GES as an innovative way to highlight a premium development for the C21st business-education acumen through desk reviews. The study is conceptually framed by examining certain skill sets such as strategic thinking, social intelligence, relational and emotional intelligence and entrepreneurship to answer three main burning questions and other hypotheses. Then the study applied the causal comparative methodology with a purposive sampling technique (N=500) from CoE, GES, NTVI, and other teachers associations. Participants responded to a 30-items, researcher-developed questionnaire. Data is analyzed on the quadrant constructs and reported as ex post facto analyses of multi-variances and regressions. Multiple associations were established for statistical significance (p=0.05). Causes and effects are postulated for scientific discussions. Findings: It was found out that these quadrants are very significant in teacher development. There were significant variations in the demographic groups. However, most teachers lack considerable skills in entrepreneurship, leadership in teaching and learning, and business thinking strategies. These have significant effect on practices and outcomes. Conclusion and Recommendations: It is quite conclusive therefore that in GES teachers may need further instructions in innovations and creativity to transform knowledge-creation into business venture. In service training (INSET) has to be comprehensive. Teacher education curricula at Colleges may have to be re-visited. Teachers have the potential to raise their social capital, to be entrepreneur, and to exhibit professionalism beyond their community services. Their primal leadership focus will benefit many clienteles including students and social circles. Recommendations examined the policy implications for curriculum design, practice, innovations and educational leadership.

Keywords: emotional intelligence, entrepreneurship, leadership, quadrant intelligence (q-i), primal teacher leadership, strategic thinking, social intelligence

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Authors: Mahda Foroughi, Bruno de Anderade, Ana Pereira Roders

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Social media platforms have been increasingly used by locals and tourists to express their opinions about buildings, cities, and built heritage in particular. Most recently, scholars have been using social media to conduct innovative research on built heritage and heritage management. Still, the application of artificial intelligence (AI) methods to analyze social media data for heritage management is seldom explored. This paper investigates the potential of short texts (sentences and hashtags) shared through social media as a data source and artificial intelligence methods for data analysis for revealing the cultural significance (values and attributes) of built heritage. The city of Yazd, Iran, was taken as a case study, with a particular focus on windcatchers, key attributes conveying outstanding universal values, as inscribed on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the intersection of public participation in heritage management and social media research; 2) methodology of data collection and data analysis related to coding people's voices from Instagram and Twitter into values of windcatchers over the last ten-years; 3) preliminary findings on the comparison between opinions of locals and tourists, sentiment analysis, and its association with the values and attributes of windcatchers. Results indicate that the age value is recognized as the most important value by all interest groups, while the political value is the least acknowledged. Besides, the negative sentiments are scarcely reflected (e.g., critiques) in social media. Results confirm the potential of social media for heritage management in terms of (de)coding and measuring the cultural significance of built heritage for windcatchers in Yazd. The methodology developed in this paper can be applied to other attributes in Yazd and also to other case studies.

Keywords: social media, artificial intelligence, public participation, cultural significance, heritage, sentiment analysis

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Shyamli Singh, Ishupinder Kaur, Vinod K. Sharma

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Climate Change refers to the change in climate for extended period of time. Climate is changing from the past history of earth but anthropogenic activities accelerate this rate of change and which is now being a global issue. Increase in greenhouse gas emissions is causing global warming and climate change related issues at an alarming rate. Increasing temperature results in climate variability across the globe. Changes in rainfall patterns, intensity and extreme events are some of the impacts of climate change. Rainfall variability refers to the degree to which rainfall patterns varies over a region (spatial) or through time period (temporal). Temporal rainfall variability can be directly or indirectly linked to climate change. Such variability in rainfall increases the vulnerability of communities towards climate change. Increasing urbanization and unplanned developmental activities, the air quality is deteriorating. This paper mainly focuses on the rainfall variability due to increasing level of greenhouse gases. Rainfall data of 65 years (1951-2015) of Safdarjung station of Delhi was collected from Indian Meteorological Department and analyzed using Mann-Kendall test for time-series data analysis. Mann-Kendall test is a statistical tool helps in analysis of trend in the given data sets. The slope of the trend can be measured through Sen’s slope estimator. Data was analyzed monthly, seasonally and yearly across the period of 65 years. The monthly rainfall data for the said period do not follow any increasing or decreasing trend. Monsoon season shows no increasing trend but here was an increasing trend in the pre-monsoon season. Hence, the actual rainfall differs from the normal trend of the rainfall. Through this analysis, it can be projected that there will be an increase in pre-monsoon rainfall than the actual monsoon season. Pre-monsoon rainfall causes cooling effect and results in drier monsoon season. This will increase the vulnerability of communities towards climate change and also effect related developmental activities.

Keywords: greenhouse gases, Mann-Kendall test, rainfall variability, Sen's slope

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Authors: Khon Natalya, Kim Alla, Mukhitdinova Tansulu

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The aim of the research was to reveal the link between mental variables, such as spatial abilities, memory, intellect and professional experience of drivers. Participants were allocated within 4 groups: no experience, inexperienced, skilled and professionals (total 85 participants). Level of ability for spatial navigation and indicator of nonverbal memory grow along the process of accumulation of driving experience. At high levels of driving experience this tendency is especially noticeable. The professionals having personal achievements in driving (racing) differ from skilled drivers in better feeling of direction which is specific for them not just in a short-term situation of an experimental task, but in life-size perspective. The level of ability of mental rotation does not grow with growth of driving experience which confirms the multiple intelligence theory according to which spatial abilities represent specific, other than logical intelligence type of intellect. The link between spatial abilities, memory, intellect, and professional experience of drivers seems to be different relating spatial navigation or mental rotation as different kinds of spatial abilities.

Keywords: memory, spatial ability, intellect, drivers

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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