In traditional notarization processes, the correctness of the activities between the parties is guaranteed by a central authority or guaranteeing institution. In this case, the authority is not able to quickly establish the originality of the content to be notarized, or at least to have a large degree of certainty without the use of automated systems. This paper presents a new notarization platform that uses blockchain technology and integrates advanced anti-plagiarism approaches able to effectively detect copyright violations of documents that users want to notarize. In addition, our proposal includes the use of models, methods, and techniques, through which a very high level of privacy and information security can be guaranteed.

The smartphone is an excellent source of data; it is possible to extrapolate smartphone sensor values and, through Machine Learning approaches, perform anomaly detection analysis characterized by human behavior. This work exploits Human Activity Recognition (HAR) models and techniques to identify human activity performed while filling out a questionnaire via a smartphone application, which aims to classify users as Bullying, Cyberbullying, Victims of Bullying, and Victims of Cyberbullying. The purpose of the work is to discuss a new smartphone methodology that combines the final label elicited from the cyberbullying/bullying questionnaire (Bully, Cyberbully, Bullying Victim, and Cyberbullying Victim) and the human activity performed (Human Activity Recognition) while the individual fills out the questionnaire. The paper starts with a state-of-the-art analysis of HAR to arrive at the design of a model that could recognize everyday life actions and discriminate them from actions resulting from alleged bullying activities. Five activities were considered for recognition: Walking, Jumping, Sitting, Running and Falling. The best HAR activity identification model then is applied to the Dataset derived from the “Smartphone Questionnaire Application” experiment to perform the analysis previously described.

This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.

Cyberbullying is an intentional aggressive act carried out repeatedly through electronic means against a victim unable to defend themself. This phenomenon combines aggressive behavior, repetitive actions, and the victim's inability to defend themself with the permanent nature of online content and easy and wide dissemination. This work focuses on cyberaggression, defined as aggressive online behavior that is distinct from cyberbullying because it is not repeated. A new dataset, “Aggressive Italian Dataset,” and a text feature extraction method were developed and compared with various Shallow Learning and BERT models in Italian — the data collected from Twitter (X) and Instagram concern offenses to famous people. The goal is to expand the dataset to include comments from adult users, simulating the fundamental dynamics of online assaults. BERT appears to have lower Precision, Recall, F1-Score, and Accuracy performance than other Shallow models with our innovative Feature Engineering.