Automatic Context-Driven Inference of Engagement in HMI: A Survey

An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Several factors contribute to engagement state inference, which include the interaction context and interactants' behaviours and identity. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. This paper presents a comprehensive survey on previous work in engagement inference for human-machine interaction, entailing interdisciplinary definition, engagement components and factors, publicly available datasets, ground truth assessment, and most commonly used features and methods, serving as a guide for the development of future human-machine interaction interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement perception modules are integrated sets apart the presented survey from existing surveys

Adverse Drug Reactions among Critically Ill Patients at Cairo University Hospital: Frequency and Outcomes

Background: Adverse drug reactions (ADRs) are common problem in intensive care units where the poly pharmacy is involved in treating patients. Control of such events is possible if it is identified and reported. However, reporting of adverse drug reactions still in its infancy. Aim: the aim is to assess the frequency and outcomes of adverse drug reactions among critically ill patients at Cairo university hospital. Research questions: 1- What is the frequency of adverse drug reactions among a selected sample of critically ill patients at Cairo university hospital? 2- What is the outcome of adverse drug reactions among a selected sample of critically ill patients at Cairo university hospital? 3- What is the degree of severity of adverse drug reactions among a selected sample of critically ill patients at Cairo university hospital? Design: Descriptive exploratory design was utilized.  Setting: The study was carried out at the Critical Care Department affiliated to Cairo University Hospitals. Subjects: A convenience sample of 150 male & female critically ill adult patients receiving different types of medications constituted the study sample. Tools: Two tools were utilized in the study, 1.Sociodemographic and medical data sheet and, 2.Adverse drug reactions assessment sheet. Results: The study results revealed that one of fifth (21%) of study sample were suffered from adverse drug reactions. ADRs were represented on the patients in the form of dry mouth, abdominal distension, headache, insomnia, constipation, tachycardia, peripheral edema, hypertension, hypotension, cough, drowsiness. Severity of adverse drug reactions was ranged from mild severity (41.9%) to moderate and severe reaction (9.7%). Conclusion: The prevalence of adverse drug reactions among critically ill patients is prevalent in a ratio of nearly (21%); and, more than half of these reactions were life-threatening. Recommendation: A written hospital policy describing basic standards in management of ADRs is recommended to be established and before initiation of new medication, assess for potential drug–disease and drug–drug interactions, check dosages, and check the most common causes of ADRs, then starting new drugs. Key wards: Adverse drug reactions, poly pharmacy, critically ill patients, Frequency, Outcomes

Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

International audienceThis paper presents our response to the first interna- tional challenge on Facial Emotion Recognition and Analysis. We propose to combine different types of features to automatically detect Action Units in facial images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and an RBF kernel. During the training step, we combine these two types of features using the recently proposed SimpleMKL algorithm. SVM outputs are then averaged to exploit temporal information in the sequence. To eval- uate our system, we perform deep experimentations on several key issues: influence of features and kernel function in histogram- based SVM approaches, influence of spatially-independent in- formation versus geometric local appearance information and benefits of combining both, sensitivity to training data and interest of temporal context adaptation. We also compare our results to those of the other participants and try to explain why our method had the best performance during the FERA challenge

Combinaison de Descripteurs Hétérogènes pour la Reconnaissance de Micro-Mouvements Faciaux

Session "Posters"National audienceDans cet article, nous présentons notre réponse au premier challenge international sur la reconnaissance et l'analyse d'émotions faciales (Facial Emotion Recognition and Analysis Challenge). Nous proposons une combinaison de dif- férents types de descripteurs dans le but de détecter de manière automatique, les micro-mouvements faciaux d'un visage. Ce système utilise une Machine à Vecteurs Supports Multi-Noyaux pour chacune des Action Units (AU) que nous désirons détecter. Le premier noyau est calculé en utilisant des histogrammes de motifs binaires locaux de Gabor (ou Local Gabor Binary Pattern, LGBP) via un noyau d'intersection d'histogramme. Le second noyau quant à lui, est crée avec des coefficients de Modèles Actifs d'Apparence via un noyau gaussien. Les sorties de chacune des SVM sont ensuite filtrées dans le but d'inclure l'informa- tion temporelle de la séquence. Afin d'évaluer notre système, nous avons procédé à de nombreuses expérimentations sur plusieurs points clefs de notre méthode. Enfin, nous comparons nos résultats à ceux obtenus par les autres participants au challenge, tout en analysant nos performanche

Impact of a Designed Nursing Intervention protocol on Myocardial Infarction Patient's Outcome at a selected University Hospital in Egypt

Background: Myocardial infarction is a life threatening disease that influences the physical, psychological and social dimensions of the individual. Improper lifestyle is one of the causes of this disease.  The  designing and implementing  of  nursing  intervention protocol for MI patients could  be  one  of  the  important  and fundamental steps in improving MI patients outcomes. Aim: The aim of this study was to examine the impact of a designed nursing intervention protocol on myocardial infarction patient’s outcomes as indicated by higher post total mean knowledge scores, higher post total mean practices scores and high level of compliance to lifelong instruction. Research hypotheses: H1. Patients who will be exposed to a designed nursing intervention protocol will have a higher post total mean knowledge scores; H2. Patients who will be exposed to a designed nursing intervention protocol will have a higher post total mean practices scores; H3. Patients who will be exposed to a designed nursing intervention protocol will have a high level of compliance to lifelong instruction. Design: A quasi-experimental research design was utilized in this study Sample: A convenience sample of 40 adult male and female MI patients. Setting: The cardiac care units at a selected Cairo University Hospital were recruited to fulfill the aim of this study. Tools: Four tools were formulated& tested to collect data pertinent to the study; Socio-demographic and medical data sheet, Pre/Post knowledge questionnaire sheet, an Observational checklist and Compliance assessment sheet. Structured interview, reviewing medical records and direct observation were utilized for data collection. Results: The study results revealed that the post total mean knowledge scores of the studied subjects is increased significantly with value of t= 20.6 at p=0.000, higher post total practice scores among the studied subjects with t& p values (t=5.6 at p= 0.000 ) also, studied subjects had mild to high compliance level regarding the lifelong instructions. Conclusion: It can be concluded that, enrichment of patients' knowledge and practices in relation to their condition and utilization of the effective nursing intervention protocol as an approach of care could have a positive impact upon improvement of patients' outcome. Recommendations: The study recommended Conduction of further studies in order to assess the effectiveness of the designed protocol on patients' outcome regarding different cardiac disorders with replication of this study on a larger probability sample from different geographical locations at the Arab Republic of Egypt, in addition to establishment of cardiac rehabilitation center in the different heath care organizations. Keywards: Nursing intervention protocol, Myocardial Infarction, Outcomes, Cardiac care units

Towards Automatic Narrative Coherence Prediction

Research in Psychology has shown that stories people tell about themselves, and how they recall their experiences, reveal a lot about their individual characteristics and mental well-being. The Narrative Coherence Coding Scheme (NaCCS) is a set of guidelines established in psychology research for annotating the “coherence” of a narrative along three dimensions: context, chronology and theme. A significant correlation was found between a narrative’s coherence score and independently collected mental health markers of the narrator. Currently, all coherence annotations are done manually; a time consuming task which drains vital resources. In this paper, we propose an Artificial Intelligence based approach involving Natural Language Processing (NLP) to predict a narrative’s coherence score (4-class classification problem). We explore a number of techniques, ranging from traditional machine learning models such as Support Vector Machines (SVM) to pre-trained language models such as BERT (Bidirectional Encoder Representations from Transformers). BERT produced the best results for all dimensions in terms of accuracy: 53.7% (context), 71.8% (chronology), and 69.6% (theme). The location of information in the narratives (beginning, end, throughout) was helpful in improving predictions

Effect of Various Local Anthropogenic Impacts on the Diversity of Coral Mucus-Associated Bacterial Communities

The global continued decline in coral reefs is intensifying the need to understand the response of corals to local environmental stressors. Coral-associated bacterial communities have been suggested to have a swift response to environmental pollutants. This study aims to determine the variation in the bacterial communities associated with the mucus of two coral species, Pocillopora damicornis (Linnaeus, 1758) and Stylophora pistillata (Esper, 1792), and the coral-surrounding seawater from three areas exposed to contamination at the Jordanian coast of the Gulf of Aqaba (Red Sea), and also explores the antibacterial activity of these bacteria. Corals were collected from three contaminated zones along the coast, and the bacteria were quantified and identified by conventional morphological and biochemical tests, as well as 16S rRNA gene sequencing. The average number of bacteria significantly varied among the coral mucus from the sampling zones and between the coral mucus and the surrounding seawater. The P. damicornis mucus-associated bacterial community was dominated by members of the classes Gammaproteobacteria, Cytophagia, and Actinomycetia, while the mucus of S. pistillata represented higher bacterial diversity, with the dominance of the bacterial classes Gammaproteobacteria, Actinomycetia, Alphaproteobacteria, and Bacilli. The effects of local anthropogenic impacts on coral mucus bacterial communities were represented in the increased abundance of bacterial species related to coral diseases. Furthermore, the results demonstrated the existence of bacterial isolates with antibacterial activity that possibly acted as a first line of defense to protect and maintain the coral host against pathogens. Indeed, the dynamics of coral-associated microbial communities highlight the importance of holistic studies that focus on microbial interactions across the coral reef ecosystem

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Modélisation Multi-Objet du visage

Cette thèse traite la problématique liée à la modélisation du visage dans le but de l’analyse faciale.Dans la première partie de cette thèse, nous avons proposé le Modèle Actif d’Apparence Multi-Objet. La spécificité du modèle proposé est que les différentes parties du visage sont traités comme des objets distincts et les mouvements oculaires (du regard et clignotement) sont extrinsèquement paramétrées.La deuxième partie de la thèse porte sur l'utilisation de la modélisation de visage dans le contexte de la reconnaissance des émotions.Premièrement, nous avons proposé un système de reconnaissance des expressions faciales sous la forme d’Action Units. Notre contribution porte principalement sur l'extraction des descripteurs de visage. Pour cela nous avons utilisé les modèles AAM locaux.Le second système concerne la reconnaissance multimodale des quatre dimensions affectives :. Nous avons proposé un système qui fusionne des caractéristiques audio, contextuelles et visuelles pour donner en sortie les quatre dimensions émotionnelles. Nous contribuons à ce système en trouvant une localisation précise des traits du visage. En conséquence, nous proposons l’AAM Multi-Modèle. Ce modèle combine un modèle global extrinsèque du visage et un modèle local de la bouche.The work in this thesis deals with the problematic of face modeling for the purpose of facial analysis.In the first part of this thesis, we proposed the Multi-Object Facial Actions Active Appearance Model (AAM). The specificity of the proposed model is that different parts of the face are treated as separate objects and eye movements (gaze and blink) are extrinsically parameterized. This increases the generalization capabilities of classical AAM.The second part of the thesis concerns the use of face modeling in the context of expression and emotion recognition. First we have proposed a system for the recognition of facial expressions in the form of Action Units (AU). Our contribution concerned mainly the extraction of AAM features of which we have opted for the use of local models.The second system concerns multi-modal recognition of four continuously valued affective dimensions. We have proposed a system that fuses audio, context and visual features and gives as output the four emotional dimensions. We contribute to the system by finding the precise localization of the facial features. Accordingly, we propose the Multi-Local AAM. This model combines extrinsically a global model of the face and a local one of the mouth through the computation of projection errors on the same global AAM