Multimodal Affective State Recognition in Serious Games Applications

A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify the affective state of players during gameplay scenarios. More specifically, two monomodal classifiers have been designed for extracting affective state information based on facial expression and body motion analysis. For the combination of different modalities a deep model is proposed that is able to make a decision about player’s affective state, while also being robust in the absence of one information cue. In order to evaluate the performance of our methodology, a bimodal database was created using Microsoft’s Kinect sensor, containing feature vectors extracted from users' facial expressions and body gestures. The proposed method achieved higher recognition rate in comparison with mono-modal, as well as early-fusion algorithms. Our methodology outperforms all other classifiers, achieving an overall recognition rate of 98.3%

Achaiki Iatriki : official publication of the medical society of western Greece and Peloponnesus

In the current issue, the editorial by Cauchi et al. argues for eco-friendly measures in endoscopy and emphasies the role of healthcare providers in reducing waste. The editorial adeptly employs the three Rs (Reduce, Reuse, Recycle) framework to tackle waste management, offering practical solutions. The editorial by Milionis et al. focuses on the reverse cascade screening for paediatric familial hypercholesterolaemia (FH), which is an upcoming tool for public health. Advantages, practices, and challenges regarding FH are thoroughly discussed. Lastly, the editorial by Fousekis et al. presents the main aspects of a chronic immune-mediated cutaneous disease, dermatitis herpetiformis (DH), which constitutes an extraintestinal manifestation of celiac disease, including its diagnosis, pathogenesis, and management. Moreover, this issue includes three review articles. The review article by Krontira et al. discusses the evolving data on the epidemiology, diagnostic approach and appropriate management of foreign body and caustic substance ingestion, based on updated guidelines published by gastroenterological and endoscopic societies. The review by Halliasos et al. provides data on the clinical presentation, diagnosis, and management of metastatic acute spinal cord compression, focusing on the importance of a multidisciplinary team approach, including spine surgeons, radiation oncologists, medical oncologists, palliative care clinicians, physiotherapists, and psychologists. Lastly, the review by Schinas et al. outlines the potential of immune modulation in the treatment of infections and the need for individualised approaches in the modern world of personalised medicine by examining some of the key strategies and immune-based therapies being developed to combat infectious diseases.peer-reviewe

Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques

The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime industry has changed and is now leaning towards the use of Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) for calculating its operational scenarios as a way to compensate the reduction of profit. While AI is a technology for creating intelligent systems that can simulate human intelligence, ML is a subfield of AI, which enables machines to learn from past data without being explicitly programmed. ML has been used in other industries for increasing both availability and profitability, and it seems that there is also great potential for the maritime industry. In this paper the authors compares the performance of multiple regression algorithms like Artificial Neural Network (ANN), Tree Regressor (TRs), Random Forest Regressor (RFR), K-Nearest Neighbor (kNN), Linear Regression, and AdaBoost, in predicting the output power of the Main Engines (M/E) of an ocean going vessel. These regression algorithms are selected because they are commonly used and are well supported by the main software developers in the area of ML. For this scope, measured values that are collected from the onboard Automated Data Logging & Monitoring (ADLM) system of the vessel for a period of six months have been used. The study shows that ML, with the proper processing of the measured parameters based on fundamental knowledge of naval architecture, can achieve remarkable prediction results. With the use of the proposed method there was a vast reduction in both the computational power needed for calculations, and the maximum absolute error value of prediction

Offline and Online Adaptation in Prosocial Games

Personalization and maintenance of high levels of engagement still remain two of the main challenges in the design of serious games. Towards this end, in this paper we propose a novel adaptation approach for both online and offline adaptation in prosocial games. In this paper, we describe the implementation of an artificial intelligence driven adaptation manager, whose purpose is to direct players towards game content the players are most likely to enjoy (measured in their engagement responses). As a consequence, we demonstrate how the adaptation manager can be used to increase the chances of players attaining the game’s specific prosocial learning objectives.. Each mechanism (offline and online) processes different information about the player and concerns different types of factors affecting engagement and prosocial behavior. More specifically, the online mechanism maintains a player engagement profile for game elements related to the provision of Corrective Feedback and Positive Reinforcement, in order to adapt existing game content in real time. On the other hand, off-line adaptation matches players to game scenarios according to the players’ prosocial ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manger as a tool for enhancing students’ prosocial skills development is demonstrated through a small scale experiment, under real-conditions in a school environment, using the prosocial game of Path of Trust

DCL3 and DCL4 are likely involved in the light intensity-RNA silencing cross talk in Nicotiana benthamiana

Plants have substantially invested in RNA silencing as the central defense mechanism to combat nucleotide “invaders” such as viruses, trasposable elements and transgenes. The quantity and quality of light perceived by a plant as a constant environmental stimulus refining cell homeostasis and RNA silencing mechanism seems not to be an exception. In our recent paper in BMC Plant Biology we documented that light intensity, in physiological ranges, positively affects silencing initiation and spread.1 Here, we show that virus induced gene silecing under high light conditions results in more frequent systemic silencing events of a transgene and is acompanied by elevated DCL3 and DCL4 mRNA levels. In addition, our results show that DCL3 holds a vital role in systemic silencing spread and the positive effect of light intensity on RNA silencing requires DCL4 function

Non-invasive measurement of tibialis anterior muscle temperature during rest, cycling exercise and post-exercise recovery

We introduce a non-invasive and accurate method to assess tibialis anterior muscle temperature (Tm) during rest, cycling exercise, and post-exercise recovery using the insulation disk (INDISK) technique. Twenty-six healthy males (23.6 +/- 6.2 years; 24.1 +/- 3.1 body mass index) were randomly allocated into the 'model' (n = 16) and the 'validation' (n = 10) groups. Participants underwent 20 min supine rest, 20 min cycling exercise at 60% of age-predicted maximum heart rate, and 20 min supine post-exercise recovery. In the model group, Tm (34.55 +/- 1.02 degrees C) was greater than INDISK temperature (Tid; 32.44 +/- 1.23 degrees C; p 0.05) and a strong correlation (r = 0.804; p 0.05), a strong correlation (r = 0.644; p < 0.001), narrow 95% limits of agreement (-0.06 +/- 1.51), and low percent coefficient of variation (2.24%) between Tm (34.39 +/- 1.00 degrees C) and Tm-pred (34.45 +/- 0.73 degrees C). We conclude that the novel technique accurately predicts Tm during rest, cycling exercise, and post-exercise recovery, providing a valid and cost-efficient alternative when direct Tm measurement is not feasible