Automated Affect and Emotion Recognition from Cardiovascular Signals - A Systematic Overview Of The Field

Currently, artificial intelligence is increasingly used to recognize and differentiate emotions. Through the action of the nervous system, the heart and vascular system can respond differently depending on the type of arousal. With the growing popularity of wearable devices able to measure such signals, people may monitor their states and manage their wellness. Our goal was to explore and summarize the field of automated emotion and affect recognition from cardiovascular signals. According to our protocol, we searched electronic sources (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Explore, arXiv and medRxiv) up to 31 August 2020. In the case of all identified studies, two independent reviewers were involved at each stage: screening, full-text assessment, data extraction, and quality evaluation. All conflicts were resolved during the discussion. The credibility of included studies was evaluated using a proprietary tool based on QUADAS, PROBAST. After screening 4649 references, we identified 195 eligible studies. From artificial intelligence most used methods in emotion or affect recognition were Support Vector Machines (42.86%), Neural Network (21.43%), and k-Nearest Neighbors (11.67%). Among the most explored datasets were DEAP (10.26%), MAHNOB-HCI (10.26%), AMIGOS (6.67%) and DREAMER (2.56%). The most frequent cardiovascular signals involved electrocardiogram (63.16%), photoplethysmogram (15.79%), blood volume pressure (13.16%) and heart rate (6.58%). Sadness, fear, and anger were the most examined emotions. However, there is no standard set of investigated internal feelings. On average, authors explore 4.50 states (range from 4 to 24 feelings). Research using artificial intelligence in recognizing emotions or affect using cardiovascular signals shows an upward trend. There are significant variations in the quality of the datasets, the choice of states to detect, and the classifiers used for analysis. Research project supported by program Excellence initiative - research university for the University of Science and Technology. The authors declare that they have no conflict of interest

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-beta search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.Comment: 24 pages, 7 figures, 9 tables, expanded version of paper presented at IEEE Conference on Computational Intelligence and Games (CIG) 2014 conferenc

STransE: a novel embedding model of entities and relationships in knowledge bases

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.Comment: V1: In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al., 2015). V3: A revised version of our NAACL-HLT 2016 paper with additional experimental results and latest related wor

Action planning for graph transition systems

Graphs are suitable modeling formalisms for software and hardware systems involving aspects such as communication, object orientation, concurrency, mobility and distribution. State spaces of such systems can be represented by graph transition systems, which are basically transition systems whose states and transitions represent graphs and graph morphisms. In this paper, we propose the modeling of graph transition systems in PDDL and the application of heuristic search planning for their analysis. We consider different heuristics and present experimental results

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems