One Time User Key: A User-Based Secret Sharing XOR-ed Model for Multiple User Cryptography in Distributed Systems

The generation of encrypted channels between more than two users is complex, as it is necessary to share information about the key of each user. This problem has been partially solved through the secret sharing mechanism that makes it possible to divide a secret among several participants, so that the secret can be reconstructed by a well-defined part of them. The proposed system represents an extension of this mechanism, since it is designed to be applied systematically: each user has his/her key, through which temporary keys (One Time User Keys) are generated and are used to divide the secret, corresponding to the real encryption key. The system also overcomes the concept of numerical threshold (i.e., at least n participants are required to reconstruct the secret), allowing the definition, for each encryption, of which users can access and which specific groups of users can access. The proposed model can be applied both in distributed user-based contexts and as an extension of cryptographic functions, without impacting the overall security of the system. It addresses some requirements of the European Union Council resolution on encryption and also provides a wide possibility of applications in user-based distributed systems

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

attentional pattern classification for automatic dementia detection

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Leveraging Artificial Intelligence to Fight (Cyber)Bullying for Human Well-being: The BullyBuster Project

Bullying and cyberbullying are phenomena which, due to their growing diffusion, have become a real social emergency. In this context, artificial intelligence can be a powerful weapon to identify episodes of violence and fight bullying both in the virtual and in the real world. Through machine learning, it is possible to detect the language patterns used by bullies and their victims and develop rules to detect cyberbullying content automatically. The BullyBuster project merges the know-how of four interdisciplinary research groups to develop a framework useful for maintaining psycho-physical well-being in educational contexts

Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis

Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification