This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and supress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.Recently, significant work has emerged in affective computing that uses probabilistic reasoning

Abstract-Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.

White supremacist hate speech is one of the most recently observed harmful content on social media. The critical influence of these radical groups is no longer limited to social media and can negatively affect society by promoting racial hatred and violence. Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information and the implicit nature of hate speech. Therefore, it is necessary to detect such speech automatically and in a timely manner. This research investigates the feasibility of automatically detecting white supremacist hate speech on Twitter using deep learning and natural language processing techniques. Two deep learning models are investigated in this research. The first approach utilizes a bidirectional Long Short-Term Memory (BiLSTM) model along with domain-specific word embeddings extracted from white supremacist corpus to capture the semantic of white supremacist slangs and coded words. The second approach utilizes one of the most recent language models, which is Bidirectional Encoder Representations from Transformers (BERT). The BiLSTM model achieved 0.75 F1-score and BERT reached a 0.80 F1-score. Both models are tested on a balanced dataset combined from Twitter and a Stormfront dataset compiled from white supremacist forum.

This paper proposes a novel approach to sentiment analysis that leverages work in sociology on symbolic interactionism. The proposed approach uses Affect Control Theory (ACT) to analyze readers' sentiment towards factual (objective) content and towards its entities (subject and object). ACT is a theory of affective reasoning that uses empirically derived equations to predict the sentiments and emotions that arise from events. This theory relies on several large lexicons of words with affective ratings in a three-dimensional space of evaluation, potency, and activity (EPA). The equations and lexicons of ACT were evaluated on a newly collected news-headlines corpus. ACT lexicon was expanded using a label propagation algorithm, resulting in 86,604 new words. The predicted emotions for each news headline was then computed using the augmented lexicon and ACT equations. The results had a precision of 82%, 79%, and 68% towards the event, the subject, and object, respectively. These results are significantly higher than those of standard sentiment analysis techniques.

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Background Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), is associated with significant morbidity and mortality. The clinical features of COVID-19 were mentioned in previous studies. However, risk factors for COVID-19 are not fully recognized. The aim of this study is to characterize risk factors and clinical features of COVID-19 disease in Jeddah, Saudi Arabia. Methods A retrospective, chart-review, case-control study was conducted at King Abdulaziz University, Jeddah, Saudi Arabia. Demographic, clinical, radiological, and laboratory data on patients diagnosed between March 18 and May 18, 2020 were collected and analyzed. Results We reviewed medical records on 297 suspected cases of COVID-19. Of these, 175 (59%) tested positive for COVID-19 by polymerase chain reaction (PCR) and considered as cases, while 122 (41%) tested negative and considered as control. COVID-19 positive cases were more likely to be males, and non-health care providers. Hypertension (15%), diabetes (10%) and two or more concurrent comorbidities (54.4%) were more prevalent among COVID-19 patients. Patients presented with fever, cough, and loss of taste/smell were more likely to test positive for COVID-19 ( P = 0.001, 0.008, 0.008; respectively). Radiological evidence of pneumonia was associated with confirmed COVID-19 disease ( P =  0.001). Shortness of breath and gastrointestinal symptoms were not associated with the risk of COVID-19 at presentation. On admission, white blood cells, neutrophils, lymphocytes, eosinophils, basophils, and platelets were significantly lower among COVID-19 patients compared with controls. Surprisingly, D-Dimer levels were lower among COVID-19 positive patients when compared with controls. Conclusion Male gender, hypertension, and diabetes are the most commonly observed risk factors associated with COVID-19 disease in Jeddah, Saudi Arabia. COVID-19 patient had significantly lower lymphocyte and neutrophil counts.

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Public health emergencies such as disease outbreaks and bioterrorism attacks require immediate response to ensure the safety and well-being of the affected community and prevent the further spread of infection. The standard method to increase the efficiency of mass dispensing during health emergencies is to create emergency points called points of dispensing (PODs). PODs are sites for distributing medical services such as vaccines or drugs to the affected population within a specific time constraint. These PODs need to be sited in optimal locations and have people (demand points) assigned to them simultaneously; this is known as the location-allocation problem. PODs may need to be selected to serve the entire population (full allocation) or different priority or needs groups (partial allocation). Several previous studies have focused on location problems in different application domains, including healthcare. However, some of these studies focused on healthcare facility location problems without specifying location-allocation problems or the exact domain. This study presents a survey of the PODs location-allocation problem during public health emergencies. This survey aims to review and analyse the existing models for PODs location-allocation during public health emergencies based on full and partial demand points allocation. Moreover, it compares existing models based on their key features, strengths, and limitations. The challenges and future research directions for PODs location-allocation models are also discussed. The results of this survey demonstrated a necessity to develop a variety of techniques to analyse, define and meet the demand of particular groups. It also proved essential that models be developed for different countries, including accounting for variations in population size and density. Moreover, the model constraints, such as those relating to time or prioritizing certain groups, need to be considered in the solution. Finally, additional comparative studies are required to clarify which methods or models are adequate based on predefined criteria.