How can neuroscience help everyone to live and care for our confined brains? Understanding and studying human behaviour allows us to measure the impact of such confinement on each of us and to identify those who need help. The challenges are to understand the psychological repercussions following confinement and to understand how neuroscience and neurotechnology can be very interesting tools for dealing with the health crisis. Neuroscience is changing our traditional philosophical and ethical views by providing information about the biological basis of our moral behaviour. The exception of the brain is at the heart of neuroethical thinking and discourse. Neuroethics is an interdisciplinary discipline placed at the intersection between the human sciences and the neurosciences, and aims to help understand how knowledge and research in neurosciences and neurotechnologies will affect the future of society, their impact on humans, relationships, daily life, the labour market ... but also how they can help and provide solutions and answers to the questions of citizens in a health crisis. But it's a double-edged sword, collecting and analyzing brain data in real time seems to be increasingly simple and within everyone's reach, without having the hindsight of the real meaning of these data, allowing one day to read people's thoughts, control them and manipulate them. It is becoming clear that the boundaries between medical and non-medical uses of neuroscience and neurotechnology are becoming very porous, inviting us to reflect on neuroethical issues in order to put safeguards on these uses.

Quantitative structure-activity relationship (QSAR) model is adopted to study the relationship between the chemical and physical properties of various substances and the structure. Through QSAR studies, the internal relationship between the invisible structure and the activity can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the weights of back propagation artificial neural network (BP ANN). Then, the QSAR model based on CAPSO and BP ANN is put forward, hereinafter referred to as CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm is a reliable method for screening molecular descriptors and the five molecular descriptors obtained by CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed the CAPSO BP ANN model has a good performance in predicting the pKa values of various compounds, the absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the higher prediction accuracy and correlation. The proposed hybrid intelligent model can be applied in all kinds of engineering design, prediction of physical and chemical properties and intelligent calculation.

This article assumes that collaborators who adopt a positive-sum, prosocial perspective can build trust-based relationships, which is equivalent to collaborative ability (CA). To improve a collaborator’s CA, we anticipated that content based upon a model of positive-sum collaboration, founded upon ancestral behaviours presented as social norms, might alter a zero-sum, proself, collaborator’s perspective. However, we also postulated that learner’s willingness to engage in positive-sum content may rely on pre-existing prosociality. Intervention participant’s prosociality was assessed via self-assessment of their emotional intelligence (EQ), quality of life (QoL), and goal orientation (GO). Learner’s engagement in online content was assessed by the three stage reached: examine website front matter; examine website content; learn website content. Tukey’s multiple comparison showed a statistically significant difference between stages one and three, in the mean of respondent’s self-assessed QoL. This suggests that participants who wished to learn content had, on average, higher quality of life, and potentially therefore greater prosociality. In general, these findings support a claim that any intervention designed to increase individual prosociality may need to consider the target population’s reticence to engage.

Structural system design is the process of giving form to a set of interconnected components subjected to loads and design constraints while navigating a complex design space. While safe designs are relatively easy to develop, optimal designs are not. Modern computational optimization approaches employ population based metaheuristic algorithms to overcome challenges with the system design optimization landscape. However, the choice of the initial population, or ground structure, can have an outsized impact on the resulting optimization. This paper presents a new method of generating such ground structures, using a combination of topology optimization (TO) and a novel system extraction algorithm. Since TO generates monolithic structures, rather than systems, its use for structural system design and optimization has been limited. In this paper, truss systems are extracted from topologies through morphological analysis and artificial intelligence techniques. This algorithm, and its assessment, constitutes the key contributions of this paper. The structural systems obtained are compared with ground truth solutions to evaluate the performance of the algorithms. The generated structures are also compared against benchmark designs from the literature. The results indicate that the presented truss generation algorithm produces structures comparable to those generated through metaheuristic optimization, while mitigating the need for assumptions about initial ground structures.

Machine learning research has been able to solve problems in multiple aspects. An open area of research is machine learning for solving optimisation problems. An optimisation problem can be solved using a metaheuristic algorithm, which is able to find a solution in a reasonable amount of time. However, there is a problem, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly. A solution approach is shown here, using a proposal that automatically creates metaheuristic algorithms aided by a reinforced learning approach. Based on the experiments performed, the approach succeeded in creating a metaheuristic algorithm that managed to solve a large number of different continuous domain optimisation problems. This work's implications are immediate because they describe a basis for the generation of metaheuristic algorithms in real-time.

The ability to predict physical characteristics from DNA presents significant opportunities for forensic science. Giving scientists an ability to make predictions about the donor of genetic material at a crime scene can then give investigators new intelligence leads for cold cases where DNA evidence has not identified any person of interest. However, the interpretation of this new form of intelligence requires careful analysis. The responses to an online survey, conducted in 2018-19, were used to examine how actors in the criminal justice system assess and interpret different types of DNA evidence and intelligence. The groups of focus for the survey were investigators, legal practitioners and the general public (as potential jurors). Several statistically significant effects were identified based on occupation and whether an individual had prior exposure to new DNA technology. Monitoring how those involved in interpreting reports from different types of DNA evidence and intelligence interpret them helps to ensure that decisions are made based on a sound understanding of their capabilities and limitations and may inform broader training and awareness strategies.

COVID-19 poses a significant burden to healthcare systems. Healthcare organisations with a better health innovation infrastructure have faced a reduced burden and achieved success in curbing COVID-19. In Saudi Arabia, digital technologies have played a vital role in fighting SARS-CoV-2 transmission. In this paper, we aimed to summarise the experience of optimising digital health technologies in Saudi Arabia as well as discuss capabilities and opportunities during and beyond the COVID-19 pandemic. A literature review was conducted up to September 2021 to retrieve peer-reviewed articles that critique the use of digital health technology platforms (DHTPs) in Saudi Arabia during the COVID-19 outbreak. A small but significant body of literature examined the digital response to the COVID-19 pandemic in Saudi Arabia. Health officials succeeded in optimising and maintaining a strategy to mitigate the spread of the virus via different digital technologies, such as mobile health applications, artificial intelligence, and machine learning. The quick digital response in Saudi Arabia was facilitated by governmental support and considering users and technology determinants. Future research must concentrate on establishing and updating the guidelines for using DHTPs.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

Big Data warehouses are a new class of databases that largely use unstructured and volatile data for analytical purpose. Examples of this kind of data sources are those coming from the Web, such as social networks and blogs, or from sensor networks, where huge amounts of data may be available only for short intervals of time. In order to manage massive data sources, a strategy must be adopted to define multidimensional schemas in presence of fast-changing situations or even undefined business requirements. In the paper, we propose a design methodology that adopts agile and automatic approaches, in order to reduce the time necessary to integrate new data sources and to include new business requirements on the fly. The data are immediately available for analyses, since the underlying architecture is based on a virtual data warehouse that does not require the importing phase. Examples of application of the methodology are presented along the paper in order to show the validity of this approach compared to a traditional one.

This paper describes a new generation of computational intelligence founded on the ancient idea of compassion called Artificial Compassion. The creation of Artificial Compassion is the result of two coinciding historical developments. The first is the increasing discoveries of human sciences in new fields like neuroendocrinology and psychoneuroimmunology. This provides the spark for Artificial Compassion. For example, we once thought with certainty that our brain is fixed for life but neuropsychology and a device called the fMRI have shown it is “plastic”. It changes constantly throughout our lives in response to our experiences. Remarkably, we also now know it is changed for the better through positive emotions like compassion, kindness and happiness. So, too, are the immune, endocrine, genetic, cardio and neural systems influenced and changed by our emotional experiences. This new perspective on emotions and plasticity validates much of ancient wisdom in medical systems outside the west. Long held assumptions about emotion are unsuitable for humanity. The second development is ‘machine rub off’. We are in symbiotic relation with our devices today and we are plastic. We are changed by our interactions but many people have computer rage. We need Artificial Compassion to replace computer rage with positive plasticity.

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers’ productivity. Therefore, in this paper, we evaluate ChatGPT’s performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT’s trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

The question of molecular similarity is core in cheminformatics, and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbours, and did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning, and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing ‘similar’ molecules to cluster together in an effective and interpretable way.

AGI could arise within the next decades, promising a decisive strategic advantage. This paper discusses risks, associated with the development of AGI: destabilizing effects on strategic balance, underestimating risks in the interest of victory in the race, egoistically exploiting the huge benefits by a tiny minority. Further: Developed AGI could be beyond human control. Human goals could not be implemented and an intelligence explosion to superintelligence could take place leading to a total loss of control and power. If competition for AGI is non-transparent, secret, uncontrolled and not regulated, it’s possible that risks could not be managed and would lead to catastrophic consequences. The danger corresponds to that of nuclear weapons. It is crucial that the key actors of a possible AI Race agree at an early stage on the prevention and transparent regulation of a possible AI Race - similar to measures to secure strategic stability, on arms control measures, disarmament, and prevention of the proliferation of nuclear weapons. The realization that an uncontrolled AI race can lead to the extinction of humanity - this time even independent of human will – requires analogous measures to contain, prevent, regulate and secure an AI race within the framework of AGI development.

Technology advancements are evident in the healthcare field; numerous new opportunities and applications have emerged during the last years based on embedded intelligence and related to real-time diagnosis of medical issues, tele-care and telemedicine, remote monitoring of patients, computer-assisted “smart” transportation in case of emergencies, as well as new types of remotely controlled surgical operations. This paper aims to provide an overview of the application of embedded intelligence in the field of healthcare; it gathers the main critical requirements, related technologies and research advancements up to date, and it presents the most important challenges that are yet to be faced. The main focus is given in the development and optimization of Body Area Networks (BANs) based on advanced embedded sensing devices, the optimization of smart gateways in such networks, and the provision of holistic scalable and secure solutions in the healthcare domain. In addition, the paper presents two principal use cases that stem from the combination of novel information and communication technologies with classic healthcare practices, explaining the functional and non-functional requirements, as well as the mixed criticality characteristics of the associated systems.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employee to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

World statistics demonstrate that around 970 million people around the globe suffer from mental health problems (Ritchie & Roser, 2019), a major proportion of which comprised of adolescents and young adults (UNODC, 2018). Also, because of increased mental health issues the problems like substance use, suicide, depression, anxiety, and stress are also increasing (Armstrong, 2019; Bandelow & Michaelis, 2015; Ritchie & Roser, 2018; United Nations Office on Drugs and Crime [UNODC], 2019). It is thus a dire need to address the issue. The present coneptual paper proposed the role of transpersonal gratitude, emotional intelligence, and life contentment for reducing mental health risk among the adolescents and young adults.

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Emerging technologies have a potential future impact on the developments in higher education and teaching practices at the universities. The paper is based on the project “My future colleague robot” that aims to improve the competence of university teachers in the implementation of Emerging Technologies (ETs) in the teaching practices at the university. In this paper, we identified the strengths and weaknesses, opportunities and threats that are related to the adoption of two ETs, robotics and Artificial Intelligence (AI), in higher education. Additionally, we analyzed the perceptions of faculty about these ETs. The empirical data was collected using written essays from 18 university teachers and students. Deductive and inductive approaches with thematic analysis were used for the data analysis. The findings support the idea that previous experience related to ETs can support positive attitudes and the implementations of ET in university teaching. University teachers had optimistic expectations towards ETs accepting them as part of teaching practice development, while discussion about the negative effects of ETs was negligible.

Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

Most traditional theories of intelligence have little to do with the question of whether people with high intelligence can successfully address real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, confirmation bias, and even falling for discredited beliefs such as alchemy, cold fusion, and astrology. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests (e.g., Sternberg, 2019). Similarly, Stanovich and West (2014) argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Halpern and Butler (2020) advocate for critical thinking as a better model of intelligence for addressing real-world problems than those that are based on psychometric properties of general intelligence. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem like Covid-19, which we use as an example of contemporary problems that need a new approach. Critical thinking may be an antidote for the chaos of the modern world.

1) Background: Aware that engagement in the healthcare field needs high levels of emotional intelligence, we began this study in to the relationship between engagement and emotional intelligence in nurses. The objective of this study was to determine the explanatory value of the components of emotional intelligence for engagement in a sample of nurses, and to identify which of the emotional intelligence components have the greatest explanatory value for dimensions of engagement, by sex.; 2) Methods: The final study sample was made up of 2126 working nursing professionals with a mean age of 31.66 (SD=6.66). To compile the data, an ad hoc questionnaire was used to collect sociodemographic information, and to collect professional and employment information, the Utrecht Work Engagement Scale, and the Reduced Emotional Intelligence Inventory for Adults.; 3) Results: The results show that nurses with higher levels of emotional intelligence also scored more highly in engagement, with the Interpersonal factor being the greatest predictor of engagement.; and 4) Conclusions: This study have significant practical implications for the creation of intervention programs and activities to improve the performance of nurses in the workplace.

Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.

This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within an overview of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, but also in music specifically. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonisations made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and more wider assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) the current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

The authors developed a wealth curve (bell curve) that can predict a group of individuals’ wealth based on the composite intelligence score which is calculated based on the sum of the intelligence quotient and emotional awareness score. The intelligence quotient was defined as one's ability to perform, comprehend and learn, and emotional awareness was defined as the emotional ability to recognize and make sense of emotions. To move towards the right on the X-axis of the Kabir wealth curve (e.g., accumulate more wealth), individuals must improve emotional awareness and choose a professional career path that lands higher income, calculated risk-taking, and being connected with individuals that can lead to financial deals. Similarly, those facing social injustice can accumulate more wealth by improving emotional awareness, which will help them navigate challenging environments.

Information has been and will be a vital element for a person or department groups in an organization. That is why there are technologies that help us to give them the proper management of data; Business Intelligence is responsible for bringing technological solutions that correctly and effectively manage the entire volume of necessary and important information for companies. Among the solutions offered by Business Intelligence are Data Warehouses, Data Mining, among other business technologies that working together achieve the objectives proposed by an organization. It is important to highlight that these business technologies have been present since the 50's and have been evolving through time, improving processes, infrastructure, methodologies and implementing new technologies, which have helped to correct past mistakes based on information management for companies. There are questions about Business Intelligence. Could it be that in the not-too-distant future it will be used as an essential standard or norm in any organization for data management, since it provides many benefits and avoids failures at the time of classifying information. On the other hand, Cloud storage has been the best alternative to safeguard information and not depend on physical storage media, which are not 100% secure and are exposed to partial or total loss of information, by presenting hardware failures or security failures due to mishandling that can be given to such information.

The medical & the dental field is a never ending field of innovations & developments and each time the reasearchers come up with something new. One such new dimension in the fields of medicine being the incorporation of Artificial intelligence assisted technologies improving diagnosis, treatmemt plan and treatment stategies. This review focusses on the application of different technologies of AI in different fields of medicine.

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Development of supervised AI algorithms requires a large amount of labeled images. Image labelling is both time-consuming and expensive. Therefore, we explored the value of e-learning derived annotations for AI algorithm development in medical imaging. Methods We have developed an e-learning platform that involves image-based single click labelling as part of the educational learning process. Ten radiology residents, as part of their residency training, trained the recognition of pneumothorax on 1161 chest X-rays in posterior-anterior projection. Using this data, multiple AI algorithms for detecting pneumothorax were developed. Classification and localization performance of the models was tested on an independent internal testing dataset and on the public NIH ChestX-ray14 dataset. Results The AI models F1 scores on the internal and the NIH dataset were 0.87 and 0.44, respectively. Sensitivity was 0.85 and 0.80 for classification and specificity 0.96 and 0.48 for classification. F1 scores were 0.72 and 0.66, sensitivity 0.72 and 0.72. False positive rate was 0.36 and 0.32 for localisation. Conclusion Our results demonstrated that e-learning derived annotations are a valuable data source for algorithm development. Further work is needed to include additional parameters such as user performance, consensus of diagnosis, and quality control in the development pipeline.

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years is posing challenges related to resource management, safety, and security. In order to ensure safe mobility and security in the smart city environment, this paper proposes a novel Artificial Intelligence (AI) based approach empowering the authorities to better visualize the threats and to help them identify the highly-reported crime zones yielding greater predictability of crime hot-spots in a smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crimes to be committed. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) exploited in each dense crime region to predict the number of crimes in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time period scenarios i.e. (a) year-wise, i.e., for each year and (b) for the whole period of ten years, using an 80:20 ratio where 80\% data was used for training and 20\% data was used for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

The full impact of COVID-19 on policing, crime and disorder is slowly being fully unraveled. However, there remains a number of areas of policing that are yet to be examined in detail. Two of these areas include the impact on the intrinsically linked, volume of police recorded intelligence reports, and the use of stop and search. In this study we examine them symbiotically and frame them in the context of the intelligence led policing model, in particular in an effort to understand how national lockdowns in the United Kingdom affected both proactive policing approaches and the underpinning intelligence cycle. To achieve this, we use data from freedom of information requests regarding the annual levels of recorded police intelligence over a 10-year period for 20 police services. To supplement this, we examine overall national monthly volumes of stop and search activity over a 5-year period. Finally, we then use a case study approach of 3 police services to further explore changes in the conduct of stop and search such as the officer defined ethnicity, grounds for search and disposal outcomes. The findings indicate that both recorded intelligence reports and stop and search increased dramatically during periods of lockdown, despite widespread decreases in crime and social mobility. Changes in proportional impact are identified for White and Black citizens, searches for controlled drugs and the no further action disposal, but these are not consistent across police services. Potential causes and implications are then discussed and again, framed within the context of the impact on the intelligence led policing model and wider policing environment.

Every year, General Practitioners (GPs) see over 13 million patients for dermatological concerns making dermatology the highest referring speciality. Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals, but they are, like human clinicians, imperfect and there may be a trade-off between sensitivity and specificity that is likely to result in false negatives. In this paper, a study is presented to explore two areas. Firstly, the aptitude of GPs to trust appropriately (or not trust) the outputs of a fictitious AI-based decision support tool when assessing skin lesions. Secondly, to identify which individual characteristics could make GPs less prone to adhere to erroneous diagnostics results and to refrain from passive adherence to AI. Findings suggest that when the AI is correct, there is a positive effect on GPs’ performance and confidence suggesting the potential to reduce referrals for benign lesions. However, when an inexperienced GP is presented with a false-negative result, they may passively deviate from their initial clinical judgement to accept the wrong diagnosis provided. AI systems will have a false-negative rate and, when adopting new technologies, this needs to be acknowledged and fed into risk-benefit discussions and considerations around additional safety measures.

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

El objetivo del siguiente artículo es presentar algunos conceptos para entender un poco más sobre qué es la inteligencia empresarial, cuál es su significado y para qué sirve, cómo ha influido en la vida de las empresas para crear sus estrategias de negocio y por qué esta herramienta . nos ayuda a tomar decisiones en una organización.

The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

Objective: Studies in Sports Psychology and Sociology have validated causality in team-sport athletes by using emotional intelligence as a variable. This study aimed to examine the causal relationship between the types of autonomy support coaching, emotional intelligence, and interruption intention as psychosocial variables among current taekwondo athletes in Korea.Methods: In this study, 217 adult or university athletes registered in the Korea Taekwondo Association in 2020 were evaluated for the type of autonomy support coaching, emotional intelligence, and interruption intention. Results: Autonomy support coaching recognized by taekwondo athletes has a negative and positive effect on interruption intention and emotional intelligence, respectively. Moreover, emotional intelligence has a negative effect on interruption intention. which revealed that autonomy support coaching has a negative effect on interruption intention through emotional intelligence.Conclusions: Such outcomes can serve as a foundation for athletes to have the opportunity to participate in sports in a mature manner and promote positive changes in sports culture. In other words, the sensibility of the athletes can be harmoniously symmetry.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. This study reviews the state-of-the-art ML applications in the biofuels’ life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx, CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

An artificial intelligence (AI)-based conversational large language model (LLM) was launched in November 2022 namely, “ChatGPT”. Despite the wide array of potential applications of LLMs in healthcare education, research and practice, several valid concerns were raised. The current systematic review aimed to investigate the possible utility of ChatGPT and to highlight its limitations in healthcare education, research and practice. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar under the term “ChatGPT”. Eligibility criteria included the published research or preprints of any type that discussed ChatGPT in the context of healthcare education, research and practice. A total of 280 records were identified, and following full screening, a total of 60 records were eligible for inclusion. Benefits/applications of ChatGPT were cited in 51/60 (85.0%) records with the most common being the utility in scientific writing followed by benefits in healthcare research (efficient analysis of massive datasets, code generation and rapid concise literature reviews besides utility in drug discovery and development). Benefits in healthcare practice included cost saving, documentation, personalized medicine and improved health literacy. Concerns/possible risks of ChatGPT use were expressed in 58/60 (96.7%) records with the most common being the ethical issues including the risk of bias, plagiarism, copyright issues, transparency issues, legal issues, lack of originality, incorrect responses, limited knowledge, and inaccurate citations. Despite the promising applications of ChatGPT which can result in paradigm shifts in healthcare education, research and practice, the embrace of this application should be done with extreme caution. Specific applications of ChatGPT in health education include the promising utility in personalized learning tools and shift towards more focus on critical thinking and problem-based learning. In healthcare practice, ChatGPT can be valuable for streamlining the workflow and refining personalized medicine. Saving time for the focus on experimental design and enhancing research equity and versatility are the benefits in scientific research. Regarding authorship in scientific articles, as it currently stands, ChatGPT does not qualify to be listed as an author unless the ICJME/COPE guidelines are revised and amended. An initiative involving all stakeholders involved in healthcare education, research and practice is urgently needed to set a code of ethics and conduct on the responsible practices involving ChatGPT among other LLMs.

Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework proposal. The paper also contributes the Cybonto ontology, formally documenting 108 constructs and thousands of cognitive-related paths based on 20 time-tested psychology theories. Finally, the paper applied 20 network centrality algorithms in analyzing the 108 constructs. The identified top 10 constructs call for extensions of current digital cognitive architectures in preparation for the DT future.

Fundamental principles of modern cities and urban planning are challenged during the COVID-19 pandemic, such as the advantages of large city size, high density, mass transport, free use of public space, unrestricted individual mobility in cities. These principles shaped the development of cities and metropolitan areas for more than a century, but currently, there are signs that they have turned from advantage to liability. Cities Public authorities and private organisations responded to the COVID-19 crisis with a variety of policies and business practices. These countermeasures codify a valuable experience and can offer lessons about how cities can tackle another grand challenge, this of climate change. Do the measures taken during the COVID-19 crisis represent a temporal adjustment to the current health crisis? Or do they open new ways towards a new type of urban development more effective in times of environmental and health crises? We address these questions through literature review and three case studies that review policies and practices for the transformation of city ecosystems mostly affected by the COVID-19 pandemic: (a) the central business district, (b) the transport ecosystem, and (c) the tourism-hospitality ecosystem. We assess whether the measures implemented in these ecosystems shape new policy and planning models for higher readiness of cities towards grand challenges. And how, based on this experience, cities should be organized to tackle the grand challenge of environmental sustainability and climate change.

Artificial Intellignece (AI) is a platform lending immense assistance in discovering and developing drugs and thus, various such approaches have been developed with the intent of simplifying and improving biomedical operations such as drug repurposing and drug discovery. In the past decade, AI-based investigation of nanomedicines, as well as non-nanomedicines has reached the clinical level. In semblance with the traditional methods of therapy, nanomedicine therapy is employed at limited doses. The study of a variety of drugs resulted in the conclusion that the effect of each drug is variable for every patient and, evaluating that perfect drug combination manually is a time-consuming as well as an inefficient treatment method. Therefore, the use of AI simplifies and reduces the time consumption in determining the perfect customized drug combination for nano-therapy. The area with the most potential for meeting this reality is to optimize the drug and dosage parameters. It is a universally known fact that cancer is dangerous and unique because of the exacting challenges it poses during treatment and, to achieve a better treatment, the therapeutic effect on each patient must be delineated even if the volume of data generated is massive. The article aims at analyzing the AI technologies that help yield results much quicker, make the analyses simple, and efficient.

This article is an effort to scrutinize the role of Information Technology development in the chronological transformation of Urban Planning domain using the exploratory research approach. In this research, it is argued that the theoretical and practical understanding of Urban Planning should absorb and integrate the bright outcome of the rise of Information and Technology to foster congruent future urban development. The article addresses the trends of transformation in the urban planning domain through the myopic lens of the expansion of information and communications technology era followed by investigating the key drivers shaping the interaction between modern-day urban planning and information technology considering both the dark and bright sides into account.

The main restriction of the Semantic Web is the difficult of the SPARQL language, that is necessary to extract information from the Knowledge Representation also known as ontology. Making the Semantic Web accessible for people who do not know SPARQL, is essential the use of friendlier interfaces and a good alternative is Natural Language. This paper shows the implementation of a friendly prototype interface to query and retrieve, by voice, information from website building with the Semantic Web tools. In that way, the end users avoid the complicated SPARQL language. To achieve this, the interface recognizes a speech query and converts it into text, it processes the text through a java program and identifies keywords, generates a SPARQL query, extracts the information from the website and read it in voice, for the user. In our work Google Cloud Speech API makes Speech-to-Text conversions and Text-to Speech conversions are made with SVOX Pico. As results, we have measured three variables: The success rate in queries, the response time of query and a usability survey. The values of the variables allows the evaluation of our prototype. Finally the interface proposed provides us a new approach in the problem, using the Cloud like a Service, reducing barriers of access to the Semantic Web for people without technical knowledge of Semantic Web technologies.

This study was conducted to investigate the correlations between social-emotional competence (SEC) and academic achievement (AA) among nursing students and to compare students’ level of each core skill of SEC (critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, and collaboration) and academic achievement (clinical performance and subjective academic achievement). A cross-sectional design was adapted. Data were collected from 195 nursing students in the junior and senior years who had participated in clinical practicum from four universities in South Korea. General characteristics, levels of critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, collaboration, and academic achievement were collected via self-reported questionnaire. Canonical correlation analysis was performed to evaluate the relationship between SEC and AA. The canonical correlation coefficient between SEC and AA was .762. Critical thinking disposition (Rs = .89), problem-solving (Rs = .86), cooperation (Rs = .80) made the most important contributions to SEC. Clinical performance (Rs=.95) and subjective AA (Rs=.57) were correlated with AA. SEC should be addressed to improve the AA of nursing students. All core skills of SEC should be regularly promoted. It is particularly urgent for nursing students to improve their creativity.

Artificial Intelligence is increasingly being used by social media platforms to tackle online hate speech. The sheer quantity of content, the speed at which is it developed and the enhanced pressure companies are facing by States to remove hate speech quickly from their platforms have led to a tricky situation. This commentary argues that automated mechanisms, which may have biased datasets and be unable to pick up on the nuances of language, should not be left unattended with hate speech as this can lead to issues of violating freedom of expression and the right to non-discrimination.

Recently, cloud technology has become popular for smart societies. The Cloud technology has made dynamical network changes by enabling the construction of a logical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake VNFs and the encryption of communication between entities. In this paper, we proposed an LW_PKI mechanism that detects a fake VNF and guarantees data security through mutual authentication between VNFs. To evaluate the LW_PKI, we built a MANO environment to test the performance of authentication and key generation for data security. In addition, we applied the artificial intelligence algorithm to detect abnormal behavior by using real attack data in the MANO environment. The LW_PKI guaranteed the reliability of a smart service by enhancing the security of the cloud environment.

To both survive and develop continuously, enterprises must overcome many kinds of competition and challenges. Cultivating employees' active and sustainable organizational citizenship behavior is important for enterprises to successfully cope with turbulence and uncertain events during their development. In this study, we investigated the development level of and factors influencing employees' organizational citizenship behavior in current organizations. By using the Belief in a Just World Scale, Organizational Citizenship Behavior Scale, and Interpersonal Intelligence Scale, we investigated 230 employees from 15 different enterprises. The results showed that belief in a just world, interpersonal intelligence, and organizational citizenship behavior were significantly positively correlated. Interpersonal intelligence played a moderating role between belief in a just world and organizational citizenship behavior; the organizational citizenship behavior of individuals with high interpersonal intelligence increased with the strengthening of the belief in a just world, and this increase was larger than that experienced by individuals with low interpersonal intelligence. This meant that under a certain level of belief in a just world, a high level of interpersonal intelligence was more conducive to promoting employees' sustainable organizational citizenship behavior.

Luminescence-based sensors for measuring oxygen concentration are widely used both in industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the standard approach, this change is related to an oxygen concentration using the Stern–Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5 % air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..

Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for teachers and instructors used in teaching and learning, but most educators can't incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor handling of tools whose lead to low production. To tackle this issue, it is essential to develop the technical and vocational education and training (TVET) system by determining the quality of technical and vocational education (TVE) teachers. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional tech-nology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were ad-ministered to TVE teachers within six technical colleges of education in north-eastern Nigeria. The data was quantitatively analyzed using traditional linear models, namely multilinear regression (MLR) and artificial intelligence (AI) models, namely artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), which were developed using MATLAB 9.3 (R2019a), while the classical linear MLR model was developed using SPSS software. The results from our classical study indicated that TVE teachers are competent in computer and some instructional technology usage and show a high correlation between competence and teaching experience and a lower correlation between competence and gender. The goodness of fit shows a good fit of the model. Future studies should examine the application of other linear and non-linear AI techniques.

By using a systems biological perspective and available literature on human social interaction, grouping, and cohesiveness, a new coherent model is proposed that integrates existing social integration and neurobiological research into a theoretical neurobiological framework of personality and social interaction. This model allows for the coherent analysis of complex social systems and interactions within them, and proposes a framework for estimating group cohesiveness and evaluating group structures in order to build and organize optimized social groups. This „Neurobiological-Associative“ model proposes two primary feedback loops, with environmental conditioning (learning) being sorted into an associative model that modulates interaction with the social environment, and which impacts the second feedback loop involving the individuals' neurobiological capacity. In this paper, the concept of neurobiological capacity is developed and based upon contemporary research on intelligence, personality, and social behavior with a focus on the oxytocin, serotonin, and dopamine systems. The basis of social exclusion and group structure is thus, expressed in the very most simple terms, neurobiological compatibility and risk assessment modulated by an internal associative model.

Background: Albumin has a pivotal role in the homeostasis of osmotic pressure and is associated with cardiovascular, nephrotic, hepatic, and nutritional diseases. The detection of hypoalbuminemia is a cornerstone for diagnosis of hidden diseases and patient deterioration. We developed and validated a deep-learning-based model (DLM) for detection of hypoalbuminemia using electrocardiography (ECG). Methods: This historical cohort study included data from consecutive patients from two hospitals. The patient data in one hospital were divided into development (82,499 ECGs from 54,248 patients) and internal validation (20,664 ECGs from 20,664 patients) datasets, whereas the patient data in the other hospital were included in only an external validation (37,421 ECGs from 37,421 patients) dataset. An DLM was developed using a 12-lead ECG signal, age, and sex from the development dataset. The endpoint was hypoalbuminemia, defined by serum albumin concentration below 3.5 g/dL. Results: During the internal and external validations, the areas under the receiver operating characteristic curve of the DLM for the detection of hypoalbuminemia were 0.887 (0.877–0.897) and 0.888 (0.880–0.896), respectively. Among the 27,400 individuals without hypoalbuminemia at the initial laboratory exam, those identified by the DLM as higher-risk patients had a significantly larger change in developing hypoalbuminemia than those in the low-risk group (7.09% vs. 1.01%, p < 0.001) during 24 months. The sensitivity map showed that the DLM focused on the T wave and QRS complex for the detection of hypoalbuminemia. Conclusions: The DLM exhibited a high accuracy for hypoalbuminemia detection and prediction using 12-, 6-, and single-lead ECGs.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the analysis of collected data. This procedure is essentially based on the comparison of lines present in the spectrum with a literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of non-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages) 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (Decision trees, Random forest, k-Nearest Neighbour, Support Vector Machine, Probabilistic Neural Network, Multi-Layer Perceptron, and Generalized Regression Neural Network), 5-fold stratified cross-validation and test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08% with average classification time of about 0.12 s is obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. Their main feature is their ability to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video generation systems to explain their execution. This work constitutes a novel approach to get explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. Finally, we apply our approach for creating a complete explainer video about the execution of HNNs on a small recognition problem.

We introduce a mission design for an interstellar expedition to nearby earth-like exoplanets, which our analysis determined to be Tau Ceti and Gliese 667C, at the time of analysis in 2013. We review the research problems in propulsion and AGI that must be addressed to launch an AI guided interstellar probe within 100 years. We propose a new semi-autonomous agent approach for intelligent control of the spacecraft. We introduce the concept of a semi-autonomous agent as having built-in safety guarantees that constrain operation. An autonomous agent case study is presented formulating the objective, constraints, AGI implementation of the agent based on Solomono's Alpha architecture, and adding specicity. We discuss the training required to reach human-level and trans-sapient levels of intelligence which corresponds to an entire crew of AI experts specialized in elds such as astrophysics, astromechanics, astrobiology, quantum physics, computer science, molecular biology, and so forth. We project the feasibility of the human-level AI technology based on empirical ndings in neuroscience, and nd that it should be feasible by 2030. We analyze Solomono's innity point hypothesis in light of Koomey's law about energy eciency of computing and nd that the trends in 2013 indicated an early singularity by 2035, which implies that we might encounter physical bottlenecks which will decelerate computing technology improvements signicantly. We recommend thus year 2040 for launching the probe by which date other required technologies will have been developed. We discuss the scenario of a virtual crew made of brain simulations, which is a bio-information based AI approach. We detail the subsystems of command and control, communication, scientic instrumentation, power, propulsion, navigation, and shielding. We propose a variation of ICAN-II/AIMStar propulsion which uses a positron source instead of anti-protons to initiate micro-fusion reactions. We combine the positron initiated fusion pulse propulsion scheme with a miniaturized version of the Daedelus fusion thruster obtaining high performance. We derive two mission proles one for fusion pulse propulsion, and the yet hypothetical Q-Thruster. Fusion thruster requires 132.2 years for Tau Ceti, and 233.2 years for Gliese 667C, while Q-Thruster takes only 42.3 years for Tau Ceti, and 62.5 years for Gliese 667C. We also discuss extended roles for intelligent interstellar probes such as self-reproduction via nanotechnology, refueling, construction, robotic bodies, and transmission of brain simulations.

The concept of spiritual intelligence is currently at the focus of scientific debate. One of the most important problems is if it is a valid intelligence that meets the criteria developed by scholars. In this paper, I look at this idea in the context of Howard Gardner's theory of multiple intelligences and show some evidence from biology, neurology, and other scientific disciplines

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

Deep neural networks (DNNs), founded on the brain's neuronal organization, can extract higher-level features from raw input. However, complex intellect via autonomous decision-making is way beyond current AI design. Here we propose an autonomous AI inspired by the thermodynamic cycle of sensory perception, operating between two information density reservoirs. Stimulus unbalances the high entropy resting state and triggers a thermodynamic cycle. By recovering the initial conditions, self-regulation generates a response while accumulating an orthogonal, holographic potential. The resulting high-density manifold is a stable memory and experience field, which increases future freedom of action via intelligent decision-making.

(1) Background: Loneliness, little studied in Nursing Homes (NHs), can affect physical and mental health. We aimed to verify the prevalence of loneliness in 5 NHs and its associated factors. (2) Methods: Cross-sectional study. Older adults aged 65 or over with preserved cognitive status were included. The De Jong Gierveld Loneliness Scale was used to assess overall, social, and emotional loneliness; sociodemographic and health-related variables were collected. The chi-square (or Fisher’s) test and logistic regression were used for bivariate and multivariate analysis respectively. (3) Results: The final sample consisted of 65 participants (81.5% female) with a mean age of 84±7.13. Prevalence of overall loneliness was 70.7% (95%CI:58.2-81.4), social loneliness 44.6% (95% CI: 33.1-56.6) and emotional loneliness 46.2% (95% CI: 34.5–58.1). Overall loneliness was associated with lower perceived quality of life (Odds Ratio-OR= 5.52, 95% CI:1.25-24.38) and NH with state subsidized places (OR=0.19, 95% CI: 0.05-0.74); social loneliness with having 0-1 children (OR=0.25, 95% CI: 0.08-0.77), and emotional loneliness with depression (OR=4.54, 95% CI: 1.28-16.08) and urinary incontinence (UI) (OR=4.65, 95% CI: 1.23-17.52). (4) Conclusions: Loneliness was present in almost 71% of residents and was associated with the type of NH and poorer quality of life, emotional loneliness with depression and UI and social loneliness with having less than 2 children.

Data is the most valuable asset of a company or organisation nowadays, for the management of such data there are several techniques used for the storage and analysis of these data, if the organisation chooses wrongly among the alternatives it could face a very costly problem. Based on the above information we will study a very important issue today in the business world, with the global economic growth has also grown the world of technology and therefore organizations or companies also need to move forward with firm and fast pace how business evolves; Its weaknesses, strengths and the need to always be at the forefront of technological tools we will dive into the subject of business intelligence or also known as Business Intelligence (BI), Datawarehouse and Data Mining which is nothing more than a group of applications and tools that allow you to extract, transform and load some data to get to get information and knowledge in order to make a quick decision, accurate and efficient in the organization to achieve the objectives outlined. Due to the fact that there are still companies or organisations that make blind decisions in their customer or strategic processes. In order to solve this problem we rely on data mining as an alternative to minimise risks, which can lead to major and valuable losses within an organisation. In the case of a private company where data mining is applied to study patterns of customer behaviour on their own parameters of location, consumption, etc.. And third party data. The search for information is profitable for the business administration. Data mining is applied as a tool for the development of marketing tactics in competitive production and industrial sectors. This technology attempts to help perceive the attachment of databases. Data mining works on a preferential level looking for patterns, behaviours, orders or groupings that can create a model that allows us to better understand the concept and help in decision making so organisations rely on different systems such as CRM, ERP and many others, but to move from just information to generate business intelligence must be centralised in a single place where you can run data analysis of all types to discover trends that help decision making that place can be mainly a Lake or a Warehouse.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.

This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in {\it The Foundations of Statistics} and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds' but cannot work in `large worlds'. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.

The vision of the community around Geothermal Power Plant (GPP) is the development of GPP should be based on sustainable development principles, without jeopardizing the quality of life and justice for communities surrounding the power plant. This research aims to: (i) identifying issues that arise as an impact of the development of GPP in the rural Tompaso, and (ii) finding solutions to the issues to minimize the conflict that arises from further GPP development in rural Tompaso and its surroundings. This study is based on the competitive intelligence (CI) research method. The results show that the development of GPP in Tompaso has a negative impact on the natural environment and social environment. The technical solutions offered include: (i) bioremediation by cultivating plants that absorb arsenic; (ii) biosulfurization and desulfurization for reducing air pollution, especially sulfur; (iii ) floods and extreme drought are managed by improving infrastructure and reforestation; (iv) social conflicts (land acquisition, working days, labor recruitment and settlement security) are solved by intensifying program dissemination to the community and involving local communities in decision making. The recommended policy is providing incentives to the local community through strategic programs for the development of human and natural resources.

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” probability.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

Finding the key factor and possible "Newton's laws" in financial markets has remained the central issue in this area. However, with the development of information and communication technologies, financial models are becoming more and more realistic but complex which is contradictory to the objective law “Greatest truths are the simplest”. Therefore, this paper attempts to discover the most critical parameter and establishes an evolutionary model which is independent of micro features. In the model, information is the only key factor and stock price is the emergence of the collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provide a new idea for the study of key factors and core structures in the financial market.

Understanding behavioral factors associated with obesity is of importance in addressing this issue. This study examined the association between cognitive restraint, uncontrolled eating, emotional eating and body mass index (BMI) and amount of food plated, consumed, leftovers, and leftover food thrown into the trash (food wasted) in early adolescent girls nine to 13 years in O’ahu, Hawai’i (n = 93). Food plated, consumed, leftovers, and food wasted were estimated using a three-day mobile food record (mFR). Weight and height were measured to compute BMI (kg/m2). The three-factor eating questionnaire provided a score from 0 to 100 for cognitive restraint, uncontrolled eating, and emotional eating. Higher scores are indicative of greater cognitive restraint, uncontrolled eating, and emotional eating. Pearson’s correlation and general linear models were computed to examine the relationship between three factor eating scores, BMI, and food plated, consumed, leftovers, and food wasted. There was no clinically significant association between cognitive restraint and amount of food wasted. Cognitive restraint was positively correlated with BMI (r=0.36, p<0.001) and with BMI z score (r=0.40, p<0.001). Uncontrolled eating and emotional eating were positively correlated with amount of leftover food at dinner (r=0.30, p=0.006; r=0.33, p=0.003, respectively). Emotional eating was positively associated with percentage of leftover food at dinner (r=0.24, p=0.30). Additional research should examine the specific roles of cognitive restraint, uncontrolled eating, emotional eating and food waste in the development of obesity in adolescents.

If the development of machine learning and artificial intelligence plays a role in many fields of research and technology today, it has a special relationship with neurosciences. Indeed, historically inspired by our knowledge of the brain, deep learning shares some vocabularies with neurosciences and can sometimes be considered a brain’s model. Taking the particular example of epileptic seizure, which can develop in any biological neural tissue, we raise the question if and how the models used for deep learning can capture or model these pathological events. This particular example is a starting point to discuss the nature, limits, and functions of these models, and what we expect from a model of the brain. Finally, we argue that a pluralistic approach leading to the integrated coexistence of different models is necessary to study the brain in all its complexity.

Aggressive behavior in adolescence is influenced by a diversity of individual, family and social variables. The purpose of this study was to analyze the relationship between family functioning, emotional intelligence and values for development of different types of aggression, as well as to establish profiles according to the predictor variables of aggression. To do this, a sample of 317 high school students aged 13 to 18 were administered the Peer Conflict Scale, the Family Functionality Scale, the Brief Emotional Intelligence Inventory for Senior Citizens and the Values for Adolescent Development Scales. The study showed that stress management, positive adolescent development and family functioning predominated in nonaggressive subjects with higher scores than aggressors. There was also a negative relationship between the different types of aggression and emotional intelligence, positive values and family functioning. In addition, two different profiles were found. The first had low scores on all the variables, while the second profile had higher scores on all the variables except family functioning which was higher.

Germplasm is a long-term resource management mission and investment for civilization. For both food and nutritional health, the present changing environmental scenario has become an urgent universal concern. Multiple excellent studies have been previously performed, although the advancement and innovation of practices will require the exploration of the potentiality of crop germplasm. In this study, we emphasized (i) germplasm activates, current challenges and ongoing trends of the crop germplasm, and (ii) how the system biology will be helpful to understand the complex traits such as water use efficiency (WUE), and nitrogen use efficiency (NUE) to mitigate challenges for sustainable development under growing food requirement and climate change conditions. We focused on a vision for transforming PGR into a bio-digital resource system, for the development of climate-smart crops for sustainable food production. Moreover, this review attempted to address current challenges, research gaps and describe the advanced integrated strategies that could provide a platform for future crop improvement research.

This paper is an attempt to forecast the potential application of Digital Twin in an industrial environment by adopting scenario analysis as part of technology monitoring. In recent years Digital Twin has become one of the most emerging topics in both the tech industry and academia. This field is the latest addition to industry 4.0. It has paved a great growth opportunity in the fields of Healthcare, manufacturing, and smart city areas. The Digital twin can be defined as mirroring the physical entities into a virtual environment where the status, health, and control of these entities can be monitored and controlled. It is a field where Artificial intelligence and the Internet of Things play a major role in the handshake between the two environments. In this paper we have done a thorough analysis to find out the possible future outcome of this technology in the manufacturing industry from the perspective of our fictious company. These scenarios expose various challenges and opportunities our organization may face in our future endeavour.

The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

Although our emotional connection with the physical urban setting is often valued, it is rarely recognised or used as a resource to understand future actions in city planning. Yet, despite the importance of emotion, citizens’ emotions are typically seen as difficult to quantify and individualistic, even though knowledge about people’s response to space could help planners understand people’s behaviours and learn about how citizens use and live in the city. The study explores the relationship between the physical space and emotions through identifying the links between stress levels, and specific features of the urban environment. This study aims to show the potential of integrating the use of galvanic skin response (GSR) within urban spatial analysis and city planning, in order to address the relationship between emotions and urban spaces. This method involved participants using a (GSR) device linked to location data to measure participant’s emotional responses along a walking route in a city centre environment. Findings show correlations between characteristics of environment and stress levels, as well as how specific features of the city spaces such as road crossing create stress ‘hotspots’. We suggest that the data obtained could contribute to citizens creating their own information layer - an emotional layer- that could inform urban planning decision-making. The implications of this application of this method as an approach to public participation in urban planning are also discussed.

The use of alcohol and tobacco is related to several variables which act as risk or protective factors, depending on the circumstances. The objectives of this study were to analyze the relationship between emotional intelligence, resilience and family functioning in adolescent use of alcohol and tobacco and to find emotional profiles for their use considering self-concept. The sample was made up of 317 high school students aged 13 to 18 who filled out the Brief Emotional Intelligence Inventory, the Resilience Scale for Adolescents, the APGAR Scale, the Alcohol Expectancy Questionnaire - Adolescents and the Five-Factor Self-Concept Questionnaire. The results revealed that emotional intelligence and resilience, specifically, stress management and family cohesion were significant in the group of nonusers. Family functioning acts as a predictor factor for onset of use of tobacco and alcohol. Positive expectancies about drinking alcohol were found to be a risk factor and the intrapersonal factor to be protective. Both stress management and family cohesion were protective factors against smoking. Furthermore, cluster analysis revealed emotional profiles for users of both substances based on self-concept. Finally, the importance of the direction of the relationship between the variables studied for intervention in this problem should be mentioned. Responsible use by improving adolescent decision-making is one of the results expected from this type of intervention.

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago, with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines, flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research.

One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

The COVID-19 was described as a respiratory illness, however further studies recognize it as a complex heterogeneous multisystemic disorder. Global efforts have been proposed to combat COVID-19, emerging diverse therapeutic options, in which discovering new drug therapies, development of vaccines and drug repurposing have been considered the most promising approaches to fight the virus. This study aimed to repurpose known drugs for use against the COVID-19, finding better therapeutic options. Seventeen biological databases were used in this study. The genetic algorithm (GA) was performed for a set of drug target classes and COVID-19 proteins as input, whose drug candidates are obtained according to the target similarities found in the target-target similarity predictive network, resulting in a drug-target interaction network. Thus, recommended drugs correspond to the union of the drug subsets found during each GA execution. Twenty-eight drugs were indicated to be the best therapeutic targets for the virus, in special, the Cyclosporine drug was administered as adjuvant to steroid treatment for COVID-19 patients which showed positive outcomes, reducing mortality in moderate and severe cases. The drugs found have used to treat other diseases, evidencing that the COVID-19 is a multisystemic disorder and suggests that the viruses’ mechanism of action presents some comorbidity with other human diseases. Evidence shows that the drugs found in this research might act together to fight the virus in a broader fashion, however further studies including in vitro and in vivo experiments are needed to find the best combination of these drugs.

The outbreak of COVID-19 is a public health emergency that caused disastrous results in many countries. The global aim is to stop transmission and prevent the spread of the disease. To achieve it, every country needs to scale up emergency response mechanisms, educate and actively communicate with the public, intensify infected case finding, contact tracing, monitoring, quarantine of contacts, and isolation of cases. Responding to an emergency requires efficient collaboration and a multi-skilled approach (medical, information, statistical, political, social, and other expertise), which makes it hard to define one interface for all. As actors from different perspectives and domain backgrounds need to address diverse functions, the possibility to exchange available information quickly would be desirable. Geoportal provides an entry point to access a variety of data (geospatial data, epidemiological data) and could be used for data discovery, view, download, and transformation. It helps to deal with challenges like data analysis, confirmed cases geocoding, recognition of disease dynamics, vulnerable groups identification, and capacity mapping. Predicting and modeling the spread of infection, along with application support for communication and collaboration, are the biggest challenges. In response to all these challenges, we have established the Epidemic Location Intelligence System (ELIS) using open-source software components in the cloud, as a working platform with all the required functionalities.

This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT (OpenAI) and Bard (Google). Large language models have demonstrated unprecedented capabilities in responding to natural language prompts. The aim of this primer is to demystify the underlying theory and architecture of large language models, providing intuitive explanations for a broader audience. Learners seeking to gain insight into the technical underpinnings of large language models must sift through rapidly growing and fragmented literature on the topic. This primer brings all the main concepts into a single digestible document. Topics covered include text tokenization, vocabulary construction, token embedding, context embedding with attention mechanisms, artificial neural networks, and objective functions in model training. The primer also explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions. Finally, an introduction to the concept of prompt engineering highlights the importance of effective human-machine interaction through natural language in harnessing the full potential of artificial intelligence chatbots. This comprehensive yet accessible primer will benefit students and researchers seeking foundational knowledge and a deeper understanding of the inner workings of existing and emerging artificial intelligence models. The author hopes that the primer will encourage further responsible innovation and informed discussions about these increasingly powerful tools.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

Taking into account Bar-On’s postulations about social-emotional intelligence, the aim of the current work is to find out the differences in the five dimensions of this intelligence between children that practice organized sport and those children that do not practice it at elementary school level, to show that an increasing in the number of hours per day performing this activity causes differences in some of these dimensions. Hence, a sample of 940 children from elementary schools, ranging in age from 6 to 12 years old, attending different schools from the Autonomous Community of Extremadura (Spain), was used. Results showed that children who practiced organized sport had better coping abilities for stress, adaptability, and mood states, and that they are more emotionally intelligent than those who did not. Moreover, children who practiced for fewer hours daily (up to 2 hours) had better stress coping than those who practiced more. To conclude, it is important to promote the sport federative practice in elementary education, in order to ensure that children learn to better regulate and manage their emotions, without increasing it to an excessive number of hours per day, which may generate greater stress that might be difficult to control.

This study investigates aspects of job retention and business growth in small- and medium-sized enterprises (SMEs). It examines how employers think about retention policy in relation to business growth, in contrast to previous studies’ main focus on employee motivation, job retention, and turnover. Qualitative semi-structured interviews were conducted face-to-face with Chief Executive Officers (CEOs) of Engineering and Informational Technology SMEs in South Korea. The results show that an important factor influencing job retention policies of SMEs is the motivation of employees to make voluntary effort, rather than offering them additional financial rewards or using other Human Resource Management (HRM) practices to improve individual performances. Interviewees believed that job retention and business growth are closely related and they discussed various ways of eliciting emotional contributions from employees. Unlike research on larger firms, these suggestions did not involve financial rewards. How employers think about the roles played by employees strongly influences their firm’s productivity and competitiveness. The SME CEOs examined in this study considered this issue central to their firm’s survival and growth. This study suggests SME employers adjust their retention policy correspondingly.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

Functional Connectivity analysis using Electroencephalography signals is common. The EEG signals are converted to networks by transforming the signals into a correlation matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented on the correlation matrix data to classify them either on their psychometric assessment or the effect of therapy; The EEG data is trail-based/event-related. The classifications based on RNN provided higher accuracy( 74-88%) than the other three models( 50-78%). Instead of using individual graph features, a correlation matrix provides an initial test of the data. When compared with the time-resolved correlation matrix, it offered a 4-5% higher accuracy. The time-resolved correlation matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the correlation matrix, a static feature.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We present results for sensor validation and disease classification via Machine Learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches and within a 15-min examination. Symptoms and medical history were captured on the paired smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers but it remains challenging for distinguishing similar disorders.

Air pollution contributes to a critical environmental problem in various towns and cities. With the emergence of the smart cities concept, appropriate methods to curb associated with exposure to pollutants must have been a portion of appropriate urban development policy. This study presents a technologically driven air quality solution in smart cities to advertise energy-efficient and cleaner sequestration in these areas. It aims to address the issue of how to integrate the data-based strategies and artificial intelligence into efficient public sector pollution management in smart cities as a core part of the smart city definition. Exploratory research has been used in 152 smart cities, and environmental experts contributed to this study. It further addresses the technical criteria for implementing such a framework that the public administration uses to prepare the renovation of public buildings, minimize energy use and costs, and link these smart police stations to monitor air pollution as a part of integrated cities. Such a digital transition in resource management will increase public governance's energy performance, a higher standard of operation, and a healthier environment.