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Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

This article deals with the development of “Smart” technology, which is being developed on the basis of financial content management. Nowadays the evolution of smart technologies is impossible without the Internet and human intelligence. These two phenomena have become interconnected and formed a new definition, which was given the term “content management”. As the world began to undergo social transformation in the digital age, finance has become an integral part of it. A completely new definition of “financial content management” has appeared, which was not previously known to mankind. In the period of global digitalization society needs the development characteristic of today. In the world of finance, this is accompanied by processes that have received the term “smart technologies”. These smart technologies create new processes that are combined with the development of society, and also have a significant impact on finance as a separate industry. The article formulates the main idea of the future development of financial content management and the relationship of smart technologies with finance. It has been proven that smart technologies are an integral part of the development of the future society, and “financial content management” is a target component of finance.

In the past 20 years, the orthodontic profession has adopted digital technologies such as computer-assisted tooth movement, automated staged dental aligner production, and 3D simulations. Until recently, the use of artificial intelligence (AI) was limited to narrow intelligence and supervised “learning” such as automated cephalometric point recognition, segmentation of teeth from 3D files, and staging of orthodontic treatment. The next step will be to create neural networks based on general intelligence (the human intelligence is considered general intelligence), where the network using powerful computers and complex algorithms will “learn” orthodontic diagnosis and treatment planning to suggest the most appropriate treatment plan for optimized treatments and more predictable outcomes. The objectives of this paper are to describe the state of the art in AI and orthodontics and explore potential avenues for future developments and applications.

La inteligencia humana es un rasgo poligénico (~1000 genes) con una influencia de cada gen aproximadamente ascendente al 0,1%. Es un atributo indispensable para el desarrollo personal, familiar, social y económico y tiene, además, una relación directamente proporcional al mantenimiento de la salud y a una mayor esperanza de vida. La discapacidad intelectual, consecuentemente, afecta todas estas áreas y constituye un problema de salud pública en varios países de Latinoamérica en los que exhibe una prevalencia mayor al 10%. La etiología de la discapacidad intelectual sea aislada o sindrómica, es genética hasta en un 85% de los casos; se diagnostica mediante las nuevas tecnologías de búsqueda en el genoma, tales como la secuenciación masiva y el análisis cromosómico por micromatrices. El diagnóstico etiológico de la discapacidad intelectual permite la selección de terapias específicas, la determinación del pronóstico y de riesgos de recurrencia familiar e individual.

Introduction and Aim: Human intelligence is a cognitive process and was correlated with several anthropometric parameters. Height is one of the well-known factor that is suspected for a positive correlation with intelligence and their association is studied since many years. Similarly, obesity which has become a major health outcome of today is said to have a negative effect on intelligence. Prevalence of obesity and its co-morbidities are in a great rise in both developed and developing countries. Recent findings show that obesity could impair cognitive performance. This study focuses on the correlation of the parameters such as height and BMI(Body Mass Index) with intelligence for selected samples and underscores the significance of correlation. The objective of our study was to test whether a linear relationship exists between the IQ and the two parameters and their strength of correlation. Materials and Methods: The present study was conducted at Saveetha Medical College and Hospital, Chennai. Sample consists of 50 students of age group 18-20 of phase I MBBS students and the present study was directed in the Department of Anatomy. Intelligence was measured by Standard Progressive Matrices (SPM), a non-verbal reasoning test. Height was measured in metres using stadiometer. Using height and weight values obtained from the sample, Body Mass Index (weight in kg/height in metre squares) was calculated. Mean and Standard Deviation of parameters were assessed and correlation value were calculated. Results: The data were statistically analysed using correlation and regression. It showed positive correlation between height and intelligence (r=0.268) (p=0.05) and a negative correlation between obesity and intelligence (r= -0.324) (p=0.05). Conclusion: Therefore, it concludes that height and intelligence were positively correlated, concomitantly obesity and intelligence were negatively correlated mainly due to increase in BMI has a deteriorating effect on intelligence.

The COVID-19 pandemic has affected many aspects of our lives, including education. Due to this unexpected catastrophe, education has shifted to virtual-learning and auto-grading models in most parts of the world. This study explores the validity and appropriateness of auto-grading-assessment for online exams by comparing students’ online exam scores where they are first auto-graded and then manually graded. Furthermore, it investigates whether the mean differences in their scores are statistically significant. The study included two calculus courses taught by the authors, during the spring semester 2019-2020 at a private university in Saudi Arabia. The online exam was performed on the WebAssign platform, which has built-in calculus questions. The sample consisted of fifty-five students who were registered on those calculus courses. The quantitative data was analysed using the SPSS statistical tool. A paired t-test at an alpha level of 0.05 was performed on differences in mean exam scores between auto-graded and manually-graded scores. The statistical analysis results revealed a statistically significant difference in students' mean scores. Our findings illustrate the importance of human intelligence, its role in assessing students' achievements and understanding of mathematical concepts, and the extent to which instructors can currently rely on auto-grading. A careful manual investigation of auto-graded exams revealed different types of mistakes committed by students. Those mistakes were characterized into two categories: non-mathematical mistakes (related to Platform Design) and minor mathematical mistakes, which might deserve partial credit. The study indicated a need to reform the auto-grading system and provided some suggestions to overcome its setbacks. https://doi.org/10.26803/ijlter.20.3.16

Artificial Intelligence (AI) is a broad discipline that uses powerful algorithms to emulate important aspects of human intelligence. Provided by the Industry 4.0 revolution, AI is increasingly appl...

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system’s architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system’s performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical ‘grand challenges’ pursued by the AI research community over the past few decades. We suggest that such challenges lie in the ‘comfort zone’ of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress. An artificial intelligence system that can engage in a competitive debate with humans is presented.

Not many people seem to understand what it is to mimic human intelligence successfully irrespective – that is, irrespective of internal states such as intentions. Successful mimicking will involve embedding in human societies. AI practitioners have concerned themselves with reproducing something that can mimic an individual human brain, failing to notice that if such a brain is to mimic human intelligence it will also have to mimic the process of becoming socialised in human society because crucial features of human intelligence are located in human societies. One notable example of this is natural language, which is continually changing and enormously flexible in ways that cannot be predicted or controlled by individuals. The fluent natural language speaker draws on society's language in the same way that a thermometer draws on the liquid in which it is embedded. The temperature that registers on the thermometer is a property of the liquid, not the thermometer, changing when the liquid warms or cools. In the same way the language spoken by a fluent individual is a property of the society in which he or she is embedded, not a property of the individual; like the thermometer, it changes when the society changes. This means that a Turing Test, based purely on linguistic performance, can be an excellent test of human-like intelligence in a machine. The nature of natural language explains some of the recent stunning successes of deep learning and explains its failures. I also try to explain why the science of AI is so poor at presenting and testing its claims. It aims at convincing the outside world of its success rather than engaging in the kind of assiduous self-criticism engaged in by the physical sciences.

I am fascinated by how rich and flexible human intelligence is. From a quick glance at the scenes in Figure 1A, we effortlessly recognize the 3D geometry and texture of the objects within, reason about how they support each other, and when they move, track and predict their trajectories. Stacking blocks, picking up fruits---we also plan and interact with scenes and objects in many ways.

Within current debates about the future impact of Artificial Intelligence (AI) on human society, roughly three different perspectives can be recognised: (1) the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; (2) the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and (3) the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: (a) a clear description for each of the three perspectives, along with their history and background; (b) an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and (c) a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society.

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuayollotl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.

Purpose The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network. Design/methodology/approach The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions. Findings The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages. Research limitations/implications The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study. Originality/value Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

Incremental implicit theories are associated with a belief regarding it is possible to improve one’s intelligence or ability through efforts. Previous studies have demonstrated that incremental implicit theories contributed to better academic achievement and positive youth development. Our study aimed to examine whether incremental implicit theories of morality significantly influenced change in students’ engagement in voluntary service activities. In our study, 54 Korean college students for Study 1 and 180 Korean 8th graders for Study 2 were recruited to conduct two two-wave studies. We surveyed participants’ implicit theories of morality and participation in voluntary service activities. The effect of implicit theories of morality on change in service engagement was analyzed through regression analysis. In Study 1, the moral growth mindset significantly moderated longitudinal change in service engagement. In Study 2, the moral growth mindset significantly influenced engagement in art-related activities, while it significantly moderated change in engagement in youth-related activities.

High intelligence is commonly regarded as an unequivocally beneficial feature in humans. Among many other desirable variables, it has been shown to be associated with better physical and mental health outcomes. However, some researchers have found that the prevalence of allergies, asthma, anxiety and other conditions is elevated in populations with exceptionally high intelligence. In the literature, such conditions have been described as physical and psychological overexcitabilities. In this study, we aim to directly replicate and extend the most comprehensive study on the topic, Karpinski, Kinase Kolb, Tetreault, and Borowski (2018). We will investigate the prevalence and underlying mechanisms of physical and psychological overexcitabilities in members of MENSA International, a society that requires members to score in the 98th percentile or above in an IQ test.

Law enforcement agencies in the UK are embracing evidence-based policing and recognise the importance of human source intelligence (HUMINT) in the decision-making process. A review of the literature identified six categories likely to impact the handling of a covert human intelligence source (CHIS) or an informant: (a) handler personality traits; (b) informant motivation; (c) rapport; (d) gaining cooperation; (e) obtaining information, and (f) detecting deception. This study sought to identify which of these categories current HUMINT practitioners considered the most when planning and conducting a meeting with an informant. A bespoke online survey was designed and disseminated to 34 practitioners using purposive and snowball sampling. Directed content analysis and thematic content analysis were conducted. Results indicate that practitioners appear most concerned with gaining co-operation (d) and detecting deception (f). Results also found an inter-connectivity between the six categories, with informant handlers often having to balance competing requirements. Implications for future research are discussed.

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

The unprecedented development of the Internet of Things (IoT), artificial intelligence (AI), and Big Data has stimulated a boom of social networks such as Twitter, WeChat, Facebook, etc., generating a huge amount of social data that are worth further analysis. Social computing has an important focus on mining the deep relationships between social organizations, networks, and media. The increasing volumes and complexities make big social data mining more and more difficult. Hybrid Human–Artificial Intelligence (H-AI) is an approach combining both human intelligence and AI, so as to handle demanding problems in a harmonious way. By adopting H-AI in social computing, it would provide more possibilities for social data analysis, relationship discovery, outlier detection, and prediction, and is proving to be an emerging and promising direction for AI and big data research.

With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human-AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user's modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog's actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model. This work was supported in part by NSFC under Grant 61972106, Grant U1636215, Grant 61871140, Grant 61572153, and Grant 61872420, in part by the National Key research and Development Plan under Grant 2019QY1406 and Grant 2018YFB0803504, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010136003 and Grant 2019B010137004

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI’s ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester’s utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.

The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions. We compare their behavior to two state-of-the-art algorithms for multi-task reinforcement learning, one that maps previous policies and encountered features to new reward functions and one that approximates value functions across tasks, as well as to standard model-based and model-free algorithms. Across three exploratory experiments and a large preregistered experiment, our results provide strong evidence for a strategy that maps previously learned policies to novel scenarios. These results enrich our understanding of human reinforcement learning in complex environments with changing task demands.