After several years of collaboration, synbrAIn has acquired SenticLab, with the goal of supporting further growth of the entire company group.
This work presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs.
SenticLab developed the best (and winning) solution for identifying tubercolosis within CT scans.
This work shows how to build predictor models to forecast users’ interaction duration and distance when interacting via touchless mid-air gestures in public.
This article includes a review of psychological models for inner speech, and a cognitive architecture to implement such capability in robots.
This work investigates classification algorithms, text vectorization and schemes to deal with data imbalance, proposing a novel cost sensitive approach.
This work describes a 28nm Complementary Metal Oxide Semiconductor Analog Front-End for fast-tracking small-diameter Muon Drift-Tube detectors.
This work presents a novel cognitive architecture for inner speech, based on the Standard Model of Mind, integrated with modules for self-talking.
This word exploits the correlation between user's affective state and the simultaneous body expressions, to automatically recognize emotions from gestures.
This work presents a calculus based on a first-order modal logic, attempting to make the existing inner speech theories suitable for robot.
This works consists in an effective algorithm for creating minimal-size sorting networks, based on incrementally constructing sets of sorting networks.
This work describes a new SXR diagnostic system called EXODUS, based on the Gas Electron Multiplier technology coupled with a novel data acquisition system.
This work studies children's interactions with large display via touchless avatar-based interface, investigating the impact of interactiong on learning.
This paper uses Self Organizing Maps, Evolutionary Algorithms and Ant Colony Systems to tackle the MinMax formulation of the Single-Depot Multiple-TSP
This work describes a touchless gesture elicitation and usability study to understand how to perform zoom actions while interacting with desktop displays.
This framework provides a web API to access and process data from coeliac patients, to be used also as a basis for diagnostic decision support systems.
An overview of novel areas of interest in pervasive displays research, based on the works presented during the 7th ACM Intl. Symposium on Pervasive Displays.
This work presents a new method that uses a bipartite graph for checking the subsumption relation for the optimal-size sorting network problem.
Using medical reference texts supported by a specific ontology, we developed Medi-test, a system to generate medical questionnaires in Romanian language.
This work proposes a novel approach in Named Entity rEcognition and Linking (NEEL) in tweets, applying similar strategies used for Question Answering (QA).
Touchless systems allow higher accessibility to information provision systems for patients with reduced mobility, thus improving tertiary prevention.
KIND‐DAMA is a modular middleware for easing the development of interactive applications based on gestural input, applicable in a plethora of scenarios.
This work presents a system able to recognize human body gestures implementing a constrained training set reduction technique, allowing for real-time execution.
This work describes a system for conveying audience emotions during live musical exhibitions, controlling a humanoid robot based on mobile apps.
This work aims at identifying the post-treatment time frame for confirming resectability or permanent unresectability in colorectal cancer liver metastases.
This work presents a novel chatbot architecture for the Italian language, implementing cognitive understanding of queries and a suitable disambiguation strategy
This paper describes how to use neural networks to detect in real time hand poses, based on data gathered from a Microsoft Kinect RGB-D sensor.
This work introduces an objective function for unsupervised clustering able to guide the search for significant features and optimal partitions.
This paper presents a hierarchical framework for automatic semantic annotation of plain text, with the goal of converting wiki pages into semantic wikis.
This work presents a general scheme based on a genetic algorithm and the particle swarm optimization heuristic to solve constraint satisfaction problems.