A bullying-severity identifier framework based on machine learning and fuzzy logic

Orientadores: Edson Luiz Ursini, Paulo Sérgio Martins PedroDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: O bullying nas escolas é um fenômeno social sério que se apresenta em todas partes do mundo e afeta crianças e adolescentes negativamente. Contudo, os programas anti-bullying das escolas não deveriam se focar em rotular aos estudantes como agressores ou vítimas, papéis tradicionais dos envolvidos em um episódio de bullying, porque aquilo produz efeitos contrários. Portanto, é necessário uma nova abordagem que permita lidar com os episódios de bullying, sem precisar de rótulos mas sim de determinar o nível de severidade, assim o pessoal da escola poderá responder a aqueles episódios apropriadamente. Os trabalhos disponíveis na literatura sobre técnicas computacionais para combater o bullying demonstraram resultados promissórios, contudo a maioria deles oferecem informação categórica como um conjunto de rótulos. O presente projeto propõe o desenvolvimento de um framework para determinar o nível de severidade das experiências de bullying narradas em textos de máximo 140 caracteres. Este framework está composto por duas partes: (1) avaliação dos textos utilizando classificadores de Support Vector Machine (SVM) desenvolvidos na literatura e (2) desenvolvimento do sistema de Lógica Fuzzy, as regras deste sistema foram definidos de acordo à literatura do bullying pelos autores deste projeto. Os resultados demonstraram que é necessário melhorar a acurácia e precisão dos classificadores SVM para conseguir determinar o nível de severidade por meio do sistema de Lógica Fuzzy. Neste trabalho, como parte das melhorias dos classificadores SVM, se rotularam novos textos para serem utilizados na fase de pré-processamento dos dados para criar novos modelos SVM e compará-los com aqueles desenvolvidos na literatura, os quais, finalmente foram utilizados em nosso frameworkAbstract: Bullying at schools is a serious social phenomenon around the world that affects the development of children negatively. However, anti-bullying programs should not focus on labeling children as bullies or victims since they could produce opposite effects. Thus, an approach to deal with bullying episodes, without labeling children, is to determine their severity, so school staff could respond them appropriately. Related works about computational techniques to fight against of bullying showed promising results but they offer categorical information as a set of labels. This work proposes a tool to determine bullying severity in texts, composed by two parts: (1) evaluation of texts using Support Vector Machine (SVM) classifiers found in literature (2) development of a Fuzzy Logic System that uses SVM classifiers outputs as its inputs to identify the bullying severity. Results show that it is necessary to improve SVM classifiers accuracy to determine bullying severity through Fuzzy Logic. As part of our work, new texts were labeled in the data-preprocessing phase in order to develop new SVM models, which were compared to those SVM classifiers found in literatureMestradoSistemas de Informação e ComunicaçãoMestra em Tecnologi

Approaches to automated detection of cyberbullying:A Survey

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely; supervised learning, lexicon based, rule based and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rules-based approaches match text to predefined rules to identify bullying and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of quality representative labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

School Discipline, Law Enforcement, And Student Outcomes

Zero tolerance discipline policies have lost favor in recent years due to concerns that they reduce offending students’ classroom time, beget further misconduct, and decrease student engagement. In the same vein, school police programs often associated with zero tolerance policies are frequently charged with increasing student involvement in the criminal justice system. Despite much discourse on the topic of school discipline, few studies have rigorously examined causal links between zero tolerance, school-based law enforcement, and student outcomes. This dissertation examines the effects of dismantling zero tolerance and reducing police officer presence in Philadelphia schools on school-level rates of student misconduct, administrative responses, and academic achievement. Quasi-experimental methods applied to the data include propensity score matching, generalized difference-in-differences analysis, comparative interrupted time series analysis, and fuzzy regression discontinuity analysis. Results suggest that dismantling zero tolerance did not affect school arrests rates or the rates of incidents involving law enforcement; and while transfers and expulsions decreased two years after the policy change, truancy increased. Limiting school police officer staff positions may have led to declines in the rates of incidents involving law enforcement and arrests, but the evidence is weak due to low statistical modeling power. Areas for future work are discussed

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019