2,925 research outputs found
Non-Verbal Communication Analysis in Victim-Offender Mediations
In this paper we present a non-invasive ambient intelligence framework for
the semi-automatic analysis of non-verbal communication applied to the
restorative justice field. In particular, we propose the use of computer vision
and social signal processing technologies in real scenarios of Victim-Offender
Mediations, applying feature extraction techniques to multi-modal
audio-RGB-depth data. We compute a set of behavioral indicators that define
communicative cues from the fields of psychology and observational methodology.
We test our methodology on data captured in real world Victim-Offender
Mediation sessions in Catalonia in collaboration with the regional government.
We define the ground truth based on expert opinions when annotating the
observed social responses. Using different state-of-the-art binary
classification approaches, our system achieves recognition accuracies of 86%
when predicting satisfaction, and 79% when predicting both agreement and
receptivity. Applying a regression strategy, we obtain a mean deviation for the
predictions between 0.5 and 0.7 in the range [1-5] for the computed social
signals.Comment: Please, find the supplementary video material at:
http://sunai.uoc.edu/~vponcel/video/VOMSessionSample.mp
Sentient Networks
In this paper we consider the question whether a distributed network of
sensors and data processors can form "perceptions" based on the sensory data.
Because sensory data can have exponentially many explanations, the use of a
central data processor to analyze the outputs from a large ensemble of sensors
will in general introduce unacceptable latencies for responding to dangerous
situations. A better idea is to use a distributed "Helmholtz machine"
architecture in which the collective state of the network as a whole provides
an explanation for the sensory data.Comment: PostScript, 14 page
A comprehensive study on disease risk predictions in machine learning
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted
Multidimensional Methods for the Formulation of Bipharmaceuticals and Vaccines
Determining and preserving the higher order structural integrity and conformational stability of proteins, plasmid DNA and macromolecular complexes such as viruses, virus-like particles and adjuvanted antigens is often a significant barrier to the successful stabilization and formulation of biopharmaceutical drugs and vaccines. These properties typically must be investigated with multiple lower resolution experimental methods, since each technique monitors only a narrow aspect of the overall conformational state of a macromolecular system. This review describes the use of empirical phase diagrams (EPDs) to combine large amounts of data from multiple high-throughput instruments and construct a map of a target macromolecule's physical state as a function of temperature, solvent conditions, and other stress variables. We present a tutorial on the mathematical methodology, an overview of some of the experimental methods typically used, and examples of some of the previous major formulation applications. We also explore novel applications of EPDs including potential new mathematical approaches as well as possible new biopharmaceutical applications such as analytical comparability, chemical stability, and protein dynamics
Acoustic Communication for Medical Nanorobots
Communication among microscopic robots (nanorobots) can coordinate their
activities for biomedical tasks. The feasibility of in vivo ultrasonic
communication is evaluated for micron-size robots broadcasting into various
types of tissues. Frequencies between 10MHz and 300MHz give the best tradeoff
between efficient acoustic generation and attenuation for communication over
distances of about 100 microns. Based on these results, we find power available
from ambient oxygen and glucose in the bloodstream can readily support
communication rates of about 10,000 bits/second between micron-sized robots. We
discuss techniques, such as directional acoustic beams, that can increase this
rate. The acoustic pressure fields enabling this communication are unlikely to
damage nearby tissue, and short bursts at considerably higher power could be of
therapeutic use.Comment: added discussion of communication channel capacity in section
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A multi-agent architecture for internet distributed computing system
This thesis presents the developed taxonomy of the agent-based distributed computing systems. Based on this taxonomy, a design, implementation, analysis and distribution protocol of a multi-agent architecture for internet-based distributed computing system was developed. A prototype of the designed architecture was implemented on Spider III using the IBM Aglets software development kit (ASDK 2.0) and the language Java
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