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The Selected Letters of John Berryman, edited by Philip Coleman and Calista McRae
Get PDFA review essay on John Berryman's correspondence, in response to a new edition of his letters
An assistance system for collision avoidance using context-sensitive prediction
Get PDFAn alert and collision avoidance system is introduced. A new method has been used to calculate a closest
point of approach, incorporating a context-sensitive prediction. Movement and routing information were used
and an approach for taking evasive action is described. When a potential collision was detected, then an
estimation was made of the direction of movement and an evasive manoeuvre was selected. A closest point
of approach was calculated between the wheelchair and any object detected in its vicinity. A linear motion
vector was calculated based on current speed, position and direction and that vector was compared with the
object position
A New Collision Avoidance System for Smart Wheelchairs Using Deep Learning
Get PDFThe work presented describes a new collision avoidance system for smart wheelchair steering using Deep Learning. The system used an Artificial Neural Network (ANN) and applied a Rule-based method to create testing and training sets. Three ultrasonic sensors were used to create an array. The sensors measured distance to the closest object to the left, right and in front of the wheelchair. Readings from the array were utilised as inputs to the ANN. The system employed Deep Learning to avoid obstacles. The driving directions considered were spin left, turn left, forward, spin right, turn right and stop. The new system drove the smart wheelchair away from obstacles. The new system provided reliable results when tested and achieved 99.17% and 97.53% training and testing accuracies respectively. The testing confirmed that the new system successfully drove a smart wheelchair away from obstacles. The system can be overridden if required. Clinical tests will be carried at Chailey Heritage Foundation
Andrea Kocsis: Digital Story Re-Telling - The Prague Palimpsest
No full textThis video introduces some easy digital storytelling tools for those historians who have just started to be interested in digital humanities. The video is part of the "Mapping and Boosting Digital Humanities in the Visegrad region" project
Using classical bit-flip correction for error mitigation in quantum computations including 2-qubit correlations
Get PDFMachine learning based psychotic behaviors prediction from Facebook status updates
Get PDFWith the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks. To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). The used ML models are trained and tested by using different combinations of features selection techniques. To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set
