Brute - Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection

Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning ofInternet communication.The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem. In Japan, members of a parent-teacher association (PTA)attempted to address the problem by scanning the Internet for cyber bullying entries. To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content. This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification. The method extracts sophisticated patterns from sentences and uses them in classification. The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-visprevious methods. Next, we implemented the method into an application forAndroid smartphones to automatically detect possible harmful content in messages. The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practic

Results of the PolEval 2019 Shared Task 6 : first dataset and Open Shared Task for automatic cyberbullying detection in Polish Twitter

In this paper we describe the first dataset for the Polish language containing annotations of harmful and toxic language. The dataset was created to study harmful Internet phenomena such as cyberbullying and hate speech, which recently dramatically gain on numbers in Polish Internet as well as worldwide. The dataset was automatically collected from Polish Twitter accounts and annotated by both layperson volunteers under the supervision of a cyberbullying and hate-speech expert. Together with the dataset we propose the first open shared task for Polish to utilize the dataset in classification of such harmful phenomena. In particular, we propose two subtasks: 1) binary classification of harmful and non-harmful tweets, and 2) multiclass classification between two types of harmful information (cyberbullying and hate-speech), and other. The first installment of the shared task became a success by reaching fourteen overall submissions, hence proving a high demand for research applying such data

Digital Safety During Online Learning: What We Do to Protect Our Student?

The article aims to uncover security risks that may occur during online learning, as well as preventive measures that can be taken to avoid these threats. This study uses a combination of two methods, namely, web mining and literature review. From the results of web mining, it is found that the website articles have not provided much explanation about efforts to protect against threats on the internet, from the results of the literature review the researchers revealed that several threats that can occur on the internet, namely phishing, scamming, fraud, cyberbullying, viruses, privacy and personal data issues, and obscene or pornographic content. This study also provides three important steps in protecting children from internet threats during online learning, including assistance in accessing internet content, education about internet safety and personal data protection, as well as an introduction to Digital Citizenship and ethics in cyberspace. Keywords:  Digital Safety, Online Learning Safety, Digital Citizenshi

CyberAid: Are your children safe from cyberbullying?

Researchers around the world have been implementing machine learning as a method to detect cyberbullying text. The machine is trained using features such as variations in texts, through social media context and interactions in a social network environment. The machine can also identify and profile users through gender or use of hate speech. In this study, we analysed different types of mobile applications that manage cyberbullying. This study proposes a mechanism, which combines the best cyberbullying detection features to fill the gaps and limitations of existing applications. The results of the study have shown that the proposed mobile application records a higher accuracy in detecting cyberbully than other available applications

The World of Dungeons and Dragons as a Therapeutic Approach to Complex Trauma

This qualitative dissertation delves into the therapeutic potential of Dungeons and Dragons (D&D) as an intervention for individuals coping with trauma and related mental health challenges. Drawing from a diverse sample of participants with varying gender identities, age groups, and D&D experience levels, this study employs a grounded theory approach to unravel the intricate interplay between D&D engagement and mental health outcomes. The study identifies several key implications, including the importance of creating inclusive therapeutic spaces, the lifespan relevance of D&D interventions, the potential for tailored interventions addressing relationship dynamics, and the accessibility of D&D as a therapeutic tool. Delimitations highlight the contextual specificity, limited generalizability, sample characteristics, subjective perceptions, therapeutic emphasis, temporal constraints, and cultural considerations inherent to this research

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research

Overcoming over–indebtedness with AI - A case study on the application of AutoML to research

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field database of 1,654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning generated three overindebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). These served as basis for a better understanding on the complex issue that is over-indebtedness. Second, a predictive model was developed to serve as a tool for policymakers and advisory services by streamlining the classification of overindebtedness profiles. On building such model, an AutoML approach was leveraged achieving performant results (92.1% accuracy score). Furthermore, within the AutoML framework, two techniques were employed, leading to a deeper discussion on the benefits and inner workings of such strategy. Ultimately, this research looks to contribute on three fronts: theoretical, by unfolding previously unexplored characteristics on the concept of over-indebtedness; methodological, by proposing AutoML as a powerful research tool accessible to investigators on many backgrounds; and social, by building real-world applications that aim at mitigating over-indebtedness and, consequently, poverty risk

Yksityisyyden turvaavia protokollia verkkoliikenteen suojaamiseen

Digital technologies have become an essential part of our lives. In many parts of the world, activities such as socializing, providing health care, leisure and education are entirely or partially relying on the internet. Moreover, the COVID-19 world pandemic has also contributed significantly to our dependency on the on-line world. While the advancement of the internet brings many advantages, there are also disadvantages such as potential loss of privacy and security. While the users enjoy surfing on the web, service providers may collect a variety of information about their users, such as the users’ location, gender, and religion. Moreover, the attackers may try to violate the users’ security, for example, by infecting the users’ devices with malware. In this PhD dissertation, to provide means to protect networking we propose several privacy-preserving protocols. Our protocols empower internet users to get a variety of services, while at the same time ensuring users’ privacy and security in the digital world. In other words, we design our protocols such that the users only share the amount of information with the service providers that is absolutely necessary to gain the service. Moreover, our protocols only add minimal additional time and communication costs, while leveraging cryptographic schemes to ensure users’ privacy and security. The dissertation contains two main themes of protocols: privacy-preserving set operations and privacy-preserving graph queries. These protocols can be applied to a variety of application areas. We delve deeper into three application areas: privacy-preserving technologies for malware protection, protection of remote access, and protecting minors.Digitaaliteknologiasta on tullut oleellinen osa ihmisten elämää. Monissa osissa maailmaa sellaiset toiminnot kuten terveydenhuolto, vapaa-ajan vietto ja opetus ovat osittain tai kokonaan riippuvaisia internetistä. Lisäksi COVID-19 -pandemia on lisännyt ihmisten riippuvuutta tietoverkoista. Vaikkakin internetin kehittyminen on tuonut paljon hyvää, se on tuonut mukanaan myös haasteita yksityisyydelle ja tietoturvalle. Käyttäjien selatessa verkkoa palveluntarjoajat voivat kerätä käyttäjästä monenlaista tietoa, kuten esimerkiksi käyttäjän sijainnin, sukupuolen ja uskonnon. Lisäksi hyökkääjät voivat yrittää murtaa käyttäjän tietoturvan esimerkiksi asentamalla hänen koneelleen haittaohjelmia. Tässä väitöskirjassa esitellään useita turvallisuutta suojaavia protokollia tietoverkossa tapahtuvan toiminnan turvaamiseen. Nämä protokollat mahdollistavat internetin käytön monilla tavoilla samalla kun ne turvaavat käyttäjän yksityisyyden ja tietoturvan digitaalisessa maailmassa. Toisin sanoen nämä protokollat on suunniteltu siten, että käyttäjät jakavat palveluntarjoajille vain sen tiedon, joka on ehdottoman välttämätöntä palvelun tuottamiseksi. Protokollat käyttävät kryptografisia menetelmiä käyttäjän yksityisyyden sekä tietoturvan varmistamiseksi, ja ne hidastavat kommunikaatiota mahdollisimman vähän. Tämän väitöskirjan sisältämät protokollat voidaan jakaa kahteen eri teemaan: protokollat yksityisyyden suojaaville joukko-operaatioille ja protokollat yksityisyyden suojaaville graafihauille. Näitä protokollia voidaan soveltaa useilla aloilla. Näistä aloista väitöskirjassa käsitellään tarkemmin haittaohjelmilta suojautumista, etäyhteyksien suojaamista ja alaikäisten suojelemista