Emotion-based System Research Articles

Adaptive Learning Systems based on ILOs of Courses

Nowadays, the use of e-learning techniques and methods is a very important challenge due to the importance of digital transformation to all countries. Firstly, the spread of the COVID-19 virus all over the world. Secondly, all students need to study their courses remotely from home to reduce the communication with others to save their life. All teachers need to engage their students effectively to study an online course, get more knowledge and high results at the end of these courses. Data mining is the best tool used to find a hidden pattern. We used an educational data mining tool to help teachers find the pros and cons of using an e-learning course with their students. We need to classify students on these online courses according to their ability to understand materials and quizzes, or assessment methods of the course, by making adaptive e-learning courses. In this paper, we will show the importance of using adaptive e-learning courses and the challenges faced by authors to build these systems, and we will list the different methods used with adaptive learning like gamification, brain-hex models, facial emotions, and we will also list a survey about other authors' techniques and methods used to find the student's learner style. We build a new proposed model of ILOs(Intended Learning Outcomes) adaptive learning with the emotion-based system to let the system find the student's learning style and build the material according to their skills and knowledge outcomes from the course and engage the use of facial emotion while taking the quiz to predict the student's results and the topics he/she needs to study more via our system to achieve high grades and knowledge. Our system finds that the visual students have the highest grades with 75%, followed by kinesthetic with 70% and the lowest grades in auditory with 50%.

FAMU: Fault-Tolerant Mutual Assisted Virtual Emotion Barrier System Using Intelligent Smart UAVs

Recently, a concept of virtual emotion barrier by wireless signal gained a lot of interests because it supports human emotion recognition within given IoT environment. If detection of human emotion can be made with possibly high detection accuracy by virtual emotion barrier, it can be utilized to an extensive scope of emotion-based applications and communication software services. But, some IoT devices failures in the barrier may cause emotion-based system performance degradation critically. Also, thanks to recent advent of technology, intelligent smart UAVs with rapid speed are considered as promising devices when existing infrastructures are recovered. In this study, fault-tolerant mutual assisted virtual emotion barrier system is introduced with intelligent autonomous UAVs in convergent smart cities. Moreover, we present a formal definition of the problem whose goal is maximizing entire total accuracy of emotion detection where the failed locations are recovered by UAVs’ movement from initial locations without any potential conflicts among UAVs. A greedy UAV movement scheme that constructs resilient virtual emotion barriers is proposed by handling those failed parts in the barriers continuously. Moreover, the proposed schemes are executed by comprehensive simulations with useful scenarios and we evaluate the earned outcome.

Interpretation and modeling of emotions in the management of autonomous robots using a control paradigm based on a scheduling variable

The paper presents a technical introduction to psychological theories of emotions. It highlights a usable idea implemented in a number of recently developed computational systems of emotions, and the hypothesis that emotion can play the role of a scheduling variable in controlling autonomous robots. In the main part of this study, we outline our own computational system of emotion – xEmotion – designed as a key structural element in the developed target device, being an Intelligent System of Decision-making (ISD) for autonomous and robotic units.The ISD system has a cognitive architecture based on the principles of human psychology. The main purpose of building such a system is to prepare a framework for autonomous units used in system engineering (Kowalczuk and Czubenko, 2011; Czubenko et al., 2015). In particular, ISD is based on the concepts of cognitive psychology (in information processing) and motivation theory, which includes the system of needs (for decision-making). The xEmotion subsystem, however, focuses on modeling an alternative approach based on emotion. The xEmotion implementation covers aspects of somatic, appraisal and evolutionary theories of emotions using fuzzy sets.In this article, we also illustrate the core emotional behavior of the ISD system using simulation. The first application is a user interface for identifying emotions and predicting human behavior. The second is an eSailor simulation, which illustrates the possible behavior of the xEmotion subsystem. The last is an xDriver simulation experiment, which is to prove the validity of the concept of using emotion-based systems, according to the SVC principle. In summary, we also discuss other possible applications of the xEmotion system.

Emotion and reasoning in human decision-making

AbstractTwo systems in the brain that are involved in emotional and economic decision-making are described. The first is an evolutionarily old emotion-based system that operates on rewards defined by the genes such as food, warmth, social reputation, and having children. Such decisions are often based on heuristics, such as being highly sensitive to losses, because a single loss might influence one's reproductive success. This is a multidimensional system with many rewards and punishers, all of which cannot be simultaneously optimized. The second route to decision-making involves reasoning, in which it is assumed that utility can be accurately assessed and logical reason can be applied, though the human brain is not naturally computationally good at logical assessment. When decisions are taken, all those factors apply, and in addition there is noise introduced into the system by the random firing times of neurons for a given mean firing rate. The implications for economic decision-making are described. In macroeconomics, it is assumed that the economy behaves like one “representative” agent who can take rational and logical decisions, and who can maximize utility over a constraint. Given the neuroscience of decision-making, the situation is more complex. The utility function may be multidimensional, the reward value along each dimension may fluctuate, the reasoning may be imperfect, and the decision-making process is subject to noise in the brain, making it somewhat random from occasion to occasion. Moreover, each individual has a different set of value functions along each dimension, with different sensitivities to different rewards and punishers, which are expressed in the different personalities of different individuals. These factors underlying the neuroscience of human decision-making need to be taken into account in building and utilizing macroeconomic theories.

Emotion-based learning: insights from the Iowa Gambling Task

Interest in the cognitive and/or emotional basis of complex decision-making, and the related phenomenon of emotion-based learning, has been heavily influenced by the Iowa Gambling Task. A number of psychological variables have been investigated as potentially important in understanding emotion-based learning. This paper reviews the extent to which humans are explicitly aware of how we make such decisions; the biasing influence of pre-existing emotional labels; and the extent to which emotion-based systems are anatomically and functionally independent of episodic memory. Review of literature suggests that (i) an aspect of conscious awareness does appear to be readily achieved during the IGT, but as a relatively unfocused emotion-based “gut-feeling,” akin to intuition; (ii) Several studies have manipulated the affective pre-loading of IGT tasks, and make it clear that such labeling has a substantial influence on performance, an experimental manipulation similar to the phenomenon of prejudice. (iii) Finally, it appears that complex emotion-based learning can remain intact despite profound amnesia, at least in some neurological patients, a finding with a range of potentially important clinical implications: in the management of dementia; in explaining infantile amnesia; and in understanding of the possible mechanisms of psychotherapy.